linkitup Link Discovery for Research Data

Rinke Hoekstra★ and Paul GrothNetwork Insitute, VU University Amsterdam★

Law Faculty, University of Amsterdam

Linkitup - Link Discovery for Research Data by Rinke HoekstraLicensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

to2Data Semantics

Semantics for Scientific Data PublishersFrom Data

linkitup Link Discovery for Research Data

Rinke Hoekstra★ and Paul GrothNetwork Insitute, VU University Amsterdam★

Law Faculty, University of Amsterdam

Linkitup - Link Discovery for Research Data by Rinke HoekstraLicensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

to2Data Semantics

Semantics for Scientific Data PublishersFrom Data

How to share, publish, access, analyse, interpret and reuse data?

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DATA

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DATA.. the fallacies (Kayur Patel)

DATASilver Bullet?

DATASilver Bullet?

http://on.wsj.com/XCajtB

DATASilver Bullet?

http://on.wsj.com/XCajtB

DATASilver Bullet?

http://on.wsj.com/XCajtB

Data’s shameful neglectResearch cannot flourish if data are not preserved and made accessible. All concerned must act accordingly.

More and more often these days, a research project’s success is measured not just by the publications it produces, but also by the data it makes available to the wider community. Pioneer-

ing archives such as GenBank have demonstrated just how powerful such legacy data sets can be for generating new discoveries — espe-cially when data are combined from many laboratories and analysed in ways that the original researchers could not have anticipated.

All but a handful of disciplines still lack the technical, institutional and cultural frameworks required to support such open data access (see pages 168 and 171) — leading to a scandalous shortfall in the sharing of data by researchers (see page 160). This deficiency urgently needs to be addressed by funders, universities and the researchers themselves.

Research funding agencies need to recognize that preservation of and access to digital data are central to their mission, and need to be supported accordingly. Organizations in the United Kingdom, for instance, have made a good start. The Joint Information Systems Committee, established by the seven UK research councils in 1993, has made data-sharing a priority, and has helped to establish a Digital Curation Centre, headquartered at the University of Edinburgh, to be a national focus for research and development into data issues. Other European agencies have also pursued initiatives.

The United States, by contrast, is playing catch-up. Since 2005, a 29-member Interagency Working Group on Digital Data has been trying to get US funding agencies to develop plans for how they will support data archiving — and just as importantly, to develop policies on what data should and should not be preserved, and what excep-tions should be made for reasons such as patient privacy. Some agen-cies have taken the lead in doing so; many more are hanging back. They should all being moving forwards vigorously.

What is more, funding agencies and researchers alike must ensure that they support not only the hardware needed to store the data, but

also the software that will help investigators to do this. One impor-tant facet is metadata management software: tools that streamline the tedious process of annotating data with a description of what the bits mean, which instrument collected them, which algorithms have been used to process them and so on — information that is essential if other scientists are to reuse the data effectively.

Also necessary, especially in an era when data can be mixed and combined in unanticipated ways, is software that can keep track of which pieces of data came from whom. Such systems are essential if tenure and promotion committees are ever to give credit — as they should — to candidates’ track-record of data contribution.

Who should host these data? Agencies and the research community together need to create the digital equivalent of libraries: institutions that can take responsibility for preserving digital data and making them accessible over the long term. The university research libraries themselves are obvious candidates to assume this role. But whoever takes it on, data preservation will require robust, long-term funding. One potentially helpful initiative is the US National Science Foundation’s DataNet programme, in which researchers are exploring financial mecha-nisms such as subscription services and membership fees.

Finally, universities and individual disciplines need to undertake a vigorous programme of education and outreach about data. Consider, for example, that most university science students get a reasonably good grounding in statistics. But their studies rarely include anything about information management — a discipline that encompasses the entire life cycle of data, from how they are acquired and stored to how they are organized, retrieved and maintained over time. That needs to change: data management should be woven into every course in science, as one of the foundations of knowledge. ■

A step too far?The Obama administration must fund human space flight adequately, or stop speaking of ‘exploration’.

After the space shuttle Columbia burned up during re-entry into Earth’s atmosphere in 2003, the board that was convened to investigate the disaster looked beyond its technical causes

to NASA’s organizational malaise. For decades, the board pointed out, the shuttle programme had been trying to do too much with too little money . NASA desperately needed a clearer vision and a better-defined mission for human space flight.

The next year, then-President George W. Bush attempted to supply that vision with a new long-term goal: first send astronauts to build

a base on the Moon, then send them to Mars. This idea immediately set off a debate that is still continuing, in which sceptics ask whether there is any point in returning to the Moon nearly half a century after the first landings. Why not go to Mars directly, or visit near-Earth asteroids, or send people to service telescopes in the deep space beyond Earth?

Yet that debate is both counter-productive — a new set of rockets could go to all of these places — and moot, because Bush’s vision never attracted the hoped-for budget increases. Indeed, a blue-riband commission reporting to US President Barack Obama this week (see page 153) finds the organizational malaise unchanged: NASA is still doing too much with too little . Without more money, the agency won’t be sending people anywhere beyond the International Space Station, which resides in low Earth orbit only 350 kilometres up. And even the ability to do that is in question: Ares I, the US rocket that would return

“Data management should be woven into every course in science.”

145

www.nature.com/nature Vol 461 | Issue no. 7261 | 10 September 2009

145-146 Editorials WF IF.indd 145145-146 Editorials WF IF.indd 145 8/9/09 14:06:408/9/09 14:06:40

Research cannot flourish if data are not preserved and made accessible. All concerned must act accordingly.

DATASilver Bullet?

http://on.wsj.com/XCajtB

Data’s shameful neglectResearch cannot flourish if data are not preserved and made accessible. All concerned must act accordingly.

More and more often these days, a research project’s success is measured not just by the publications it produces, but also by the data it makes available to the wider community. Pioneer-

ing archives such as GenBank have demonstrated just how powerful such legacy data sets can be for generating new discoveries — espe-cially when data are combined from many laboratories and analysed in ways that the original researchers could not have anticipated.

All but a handful of disciplines still lack the technical, institutional and cultural frameworks required to support such open data access (see pages 168 and 171) — leading to a scandalous shortfall in the sharing of data by researchers (see page 160). This deficiency urgently needs to be addressed by funders, universities and the researchers themselves.

Research funding agencies need to recognize that preservation of and access to digital data are central to their mission, and need to be supported accordingly. Organizations in the United Kingdom, for instance, have made a good start. The Joint Information Systems Committee, established by the seven UK research councils in 1993, has made data-sharing a priority, and has helped to establish a Digital Curation Centre, headquartered at the University of Edinburgh, to be a national focus for research and development into data issues. Other European agencies have also pursued initiatives.

The United States, by contrast, is playing catch-up. Since 2005, a 29-member Interagency Working Group on Digital Data has been trying to get US funding agencies to develop plans for how they will support data archiving — and just as importantly, to develop policies on what data should and should not be preserved, and what excep-tions should be made for reasons such as patient privacy. Some agen-cies have taken the lead in doing so; many more are hanging back. They should all being moving forwards vigorously.

What is more, funding agencies and researchers alike must ensure that they support not only the hardware needed to store the data, but

also the software that will help investigators to do this. One impor-tant facet is metadata management software: tools that streamline the tedious process of annotating data with a description of what the bits mean, which instrument collected them, which algorithms have been used to process them and so on — information that is essential if other scientists are to reuse the data effectively.

Also necessary, especially in an era when data can be mixed and combined in unanticipated ways, is software that can keep track of which pieces of data came from whom. Such systems are essential if tenure and promotion committees are ever to give credit — as they should — to candidates’ track-record of data contribution.

Who should host these data? Agencies and the research community together need to create the digital equivalent of libraries: institutions that can take responsibility for preserving digital data and making them accessible over the long term. The university research libraries themselves are obvious candidates to assume this role. But whoever takes it on, data preservation will require robust, long-term funding. One potentially helpful initiative is the US National Science Foundation’s DataNet programme, in which researchers are exploring financial mecha-nisms such as subscription services and membership fees.

Finally, universities and individual disciplines need to undertake a vigorous programme of education and outreach about data. Consider, for example, that most university science students get a reasonably good grounding in statistics. But their studies rarely include anything about information management — a discipline that encompasses the entire life cycle of data, from how they are acquired and stored to how they are organized, retrieved and maintained over time. That needs to change: data management should be woven into every course in science, as one of the foundations of knowledge. ■

A step too far?The Obama administration must fund human space flight adequately, or stop speaking of ‘exploration’.

After the space shuttle Columbia burned up during re-entry into Earth’s atmosphere in 2003, the board that was convened to investigate the disaster looked beyond its technical causes

to NASA’s organizational malaise. For decades, the board pointed out, the shuttle programme had been trying to do too much with too little money . NASA desperately needed a clearer vision and a better-defined mission for human space flight.

The next year, then-President George W. Bush attempted to supply that vision with a new long-term goal: first send astronauts to build

a base on the Moon, then send them to Mars. This idea immediately set off a debate that is still continuing, in which sceptics ask whether there is any point in returning to the Moon nearly half a century after the first landings. Why not go to Mars directly, or visit near-Earth asteroids, or send people to service telescopes in the deep space beyond Earth?

Yet that debate is both counter-productive — a new set of rockets could go to all of these places — and moot, because Bush’s vision never attracted the hoped-for budget increases. Indeed, a blue-riband commission reporting to US President Barack Obama this week (see page 153) finds the organizational malaise unchanged: NASA is still doing too much with too little . Without more money, the agency won’t be sending people anywhere beyond the International Space Station, which resides in low Earth orbit only 350 kilometres up. And even the ability to do that is in question: Ares I, the US rocket that would return

“Data management should be woven into every course in science.”

145

www.nature.com/nature Vol 461 | Issue no. 7261 | 10 September 2009

145-146 Editorials WF IF.indd 145145-146 Editorials WF IF.indd 145 8/9/09 14:06:408/9/09 14:06:40

Research cannot flourish if data are not preserved and made accessible. All concerned must act accordingly.

DATASilver Bullet?

http://on.wsj.com/XCajtB

Data’s shameful neglectResearch cannot flourish if data are not preserved and made accessible. All concerned must act accordingly.

More and more often these days, a research project’s success is measured not just by the publications it produces, but also by the data it makes available to the wider community. Pioneer-

ing archives such as GenBank have demonstrated just how powerful such legacy data sets can be for generating new discoveries — espe-cially when data are combined from many laboratories and analysed in ways that the original researchers could not have anticipated.

All but a handful of disciplines still lack the technical, institutional and cultural frameworks required to support such open data access (see pages 168 and 171) — leading to a scandalous shortfall in the sharing of data by researchers (see page 160). This deficiency urgently needs to be addressed by funders, universities and the researchers themselves.

Research funding agencies need to recognize that preservation of and access to digital data are central to their mission, and need to be supported accordingly. Organizations in the United Kingdom, for instance, have made a good start. The Joint Information Systems Committee, established by the seven UK research councils in 1993, has made data-sharing a priority, and has helped to establish a Digital Curation Centre, headquartered at the University of Edinburgh, to be a national focus for research and development into data issues. Other European agencies have also pursued initiatives.

The United States, by contrast, is playing catch-up. Since 2005, a 29-member Interagency Working Group on Digital Data has been trying to get US funding agencies to develop plans for how they will support data archiving — and just as importantly, to develop policies on what data should and should not be preserved, and what excep-tions should be made for reasons such as patient privacy. Some agen-cies have taken the lead in doing so; many more are hanging back. They should all being moving forwards vigorously.

What is more, funding agencies and researchers alike must ensure that they support not only the hardware needed to store the data, but

also the software that will help investigators to do this. One impor-tant facet is metadata management software: tools that streamline the tedious process of annotating data with a description of what the bits mean, which instrument collected them, which algorithms have been used to process them and so on — information that is essential if other scientists are to reuse the data effectively.

Also necessary, especially in an era when data can be mixed and combined in unanticipated ways, is software that can keep track of which pieces of data came from whom. Such systems are essential if tenure and promotion committees are ever to give credit — as they should — to candidates’ track-record of data contribution.

Who should host these data? Agencies and the research community together need to create the digital equivalent of libraries: institutions that can take responsibility for preserving digital data and making them accessible over the long term. The university research libraries themselves are obvious candidates to assume this role. But whoever takes it on, data preservation will require robust, long-term funding. One potentially helpful initiative is the US National Science Foundation’s DataNet programme, in which researchers are exploring financial mecha-nisms such as subscription services and membership fees.

Finally, universities and individual disciplines need to undertake a vigorous programme of education and outreach about data. Consider, for example, that most university science students get a reasonably good grounding in statistics. But their studies rarely include anything about information management — a discipline that encompasses the entire life cycle of data, from how they are acquired and stored to how they are organized, retrieved and maintained over time. That needs to change: data management should be woven into every course in science, as one of the foundations of knowledge. ■

A step too far?The Obama administration must fund human space flight adequately, or stop speaking of ‘exploration’.

After the space shuttle Columbia burned up during re-entry into Earth’s atmosphere in 2003, the board that was convened to investigate the disaster looked beyond its technical causes

to NASA’s organizational malaise. For decades, the board pointed out, the shuttle programme had been trying to do too much with too little money . NASA desperately needed a clearer vision and a better-defined mission for human space flight.

The next year, then-President George W. Bush attempted to supply that vision with a new long-term goal: first send astronauts to build

a base on the Moon, then send them to Mars. This idea immediately set off a debate that is still continuing, in which sceptics ask whether there is any point in returning to the Moon nearly half a century after the first landings. Why not go to Mars directly, or visit near-Earth asteroids, or send people to service telescopes in the deep space beyond Earth?

Yet that debate is both counter-productive — a new set of rockets could go to all of these places — and moot, because Bush’s vision never attracted the hoped-for budget increases. Indeed, a blue-riband commission reporting to US President Barack Obama this week (see page 153) finds the organizational malaise unchanged: NASA is still doing too much with too little . Without more money, the agency won’t be sending people anywhere beyond the International Space Station, which resides in low Earth orbit only 350 kilometres up. And even the ability to do that is in question: Ares I, the US rocket that would return

“Data management should be woven into every course in science.”

145

www.nature.com/nature Vol 461 | Issue no. 7261 | 10 September 2009

145-146 Editorials WF IF.indd 145145-146 Editorials WF IF.indd 145 8/9/09 14:06:408/9/09 14:06:40

Research cannot flourish if data are not preserved and made accessible. All concerned must act accordingly.

Repository Services• Data is easy to upload

• Landing page for data

• Citable reference for data

• Default licensing options

• Guarantees for long term archival

Standard Metadata• Provenance metadata

authors, title, publication date

• Content metadata free text tags, categories, links

• Metadata is locked in

• Hard to interpret the data itself

Data is the BottleneckCommon Motifs in Scientific Workflows:

An Empirical AnalysisDaniel Garijo⇤, Pinar Alper †, Khalid Belhajjame†, Oscar Corcho⇤, Yolanda Gil‡, Carole Goble†

⇤Ontology Engineering Group, Universidad Politecnica de Madrid. {dgarijo, ocorcho}@fi.upm.es†School of Computer Science, University of Manchester. {alperp, khalidb, carole.goble}@cs.manchester.ac.uk

‡Information Sciences Institute, Department of Computer Science, University of Southern California. gil@isi.edu

Abstract—While workflow technology has gained momentumin the last decade as a means for specifying and enacting compu-tational experiments in modern science, reusing and repurposingexisting workflows to build new scientific experiments is still adaunting task. This is partly due to the difficulty that scientistsexperience when attempting to understand existing workflows,which contain several data preparation and adaptation steps inaddition to the scientifically significant analysis steps. One wayto tackle the understandability problem is through providingabstractions that give a high-level view of activities undertakenwithin workflows. As a first step towards abstractions, we reportin this paper on the results of a manual analysis performed overa set of real-world scientific workflows from Taverna and Wingssystems. Our analysis has resulted in a set of scientific workflow

motifs that outline i) the kinds of data intensive activities that areobserved in workflows (data oriented motifs), and ii) the differentmanners in which activities are implemented within workflows(workflow oriented motifs). These motifs can be useful to informworkflow designers on the good and bad practices for workflowdevelopment, to inform the design of automated tools for thegeneration of workflow abstractions, etc.

I. INTRODUCTION

Scientific workflows have been increasingly used in the lastdecade as an instrument for data intensive scientific analysis.In these settings, workflows serve a dual function: first asdetailed documentation of the method (i. e. the input sourcesand processing steps taken for the derivation of a certaindata item) and second as re-usable, executable artifacts fordata-intensive analysis. Workflows stitch together a varietyof data manipulation activities such as data movement, datatransformation or data visualization to serve the goals of thescientific study. The stitching is realized by the constructsmade available by the workflow system used and is largelyshaped by the environment in which the system operates andthe function undertaken by the workflow.

A variety of workflow systems are in use [10] [3] [7] [2]serving several scientific disciplines. A workflow is a softwareartifact, and as such once developed and tested, it can beshared and exchanged between scientists. Other scientists canthen reuse existing workflows in their experiments, e.g., assub-workflows [17]. Workflow reuse presents several advan-tages [4]. For example, it enables proper data citation andimproves quality through shared workflow development byleveraging the expertise of previous users. Users can alsore-purpose existing workflows to adapt them to their needs[4]. Emerging workflow repositories such as myExperiment

[14] and CrowdLabs [8] have made publishing and findingworkflows easier, but scientists still face the challenges of re-use, which amounts to fully understanding and exploiting theavailable workflows/fragments. One difficulty in understandingworkflows is their complex nature. A workflow may containseveral scientifically-significant analysis steps, combined withvarious other data preparation activities, and in differentimplementation styles depending on the environment andcontext in which the workflow is executed. The difficulty inunderstanding causes workflow developers to revert to startingfrom scratch rather than re-using existing fragments.

Through an analysis of the current practices in scientificworkflow development, we could gain insights on the creationof understandable and more effectively re-usable workflows.Specifically, we propose an analysis with the following objec-tives:

1) To reverse-engineer the set of current practices in work-flow development through an analysis of empirical evi-dence.

2) To identify workflow abstractions that would facilitateunderstandability and therefore effective re-use.

3) To detect potential information sources and heuristicsthat can be used to inform the development of tools forcreating workflow abstractions.

In this paper we present the result of an empirical analysisperformed over 177 workflow descriptions from Taverna [10]and Wings [3]. Based on this analysis, we propose a catalogueof scientific workflow motifs. Motifs are provided through i)a characterization of the kinds of data-oriented activities thatare carried out within workflows, which we refer to as data-oriented motifs, and ii) a characterization of the different man-ners in which those activity motifs are realized/implementedwithin workflows, which we refer to as workflow-orientedmotifs. It is worth mentioning that, although important, motifsthat have to do with scheduling and mapping of workflowsonto distributed resources [12] are out the scope of this paper.

The paper is structured as follows. We begin by providingrelated work in Section II, which is followed in Section III bybrief background information on Scientific Workflows, and thetwo systems that were subject to our analysis. Afterwards wedescribe the dataset and the general approach of our analysis.We present the detected scientific workflow motifs in SectionIV and we highlight the main features of their distribution

Data is the BottleneckCommon Motifs in Scientific Workflows:

An Empirical AnalysisDaniel Garijo⇤, Pinar Alper †, Khalid Belhajjame†, Oscar Corcho⇤, Yolanda Gil‡, Carole Goble†

⇤Ontology Engineering Group, Universidad Politecnica de Madrid. {dgarijo, ocorcho}@fi.upm.es†School of Computer Science, University of Manchester. {alperp, khalidb, carole.goble}@cs.manchester.ac.uk

‡Information Sciences Institute, Department of Computer Science, University of Southern California. gil@isi.edu

Abstract—While workflow technology has gained momentumin the last decade as a means for specifying and enacting compu-tational experiments in modern science, reusing and repurposingexisting workflows to build new scientific experiments is still adaunting task. This is partly due to the difficulty that scientistsexperience when attempting to understand existing workflows,which contain several data preparation and adaptation steps inaddition to the scientifically significant analysis steps. One wayto tackle the understandability problem is through providingabstractions that give a high-level view of activities undertakenwithin workflows. As a first step towards abstractions, we reportin this paper on the results of a manual analysis performed overa set of real-world scientific workflows from Taverna and Wingssystems. Our analysis has resulted in a set of scientific workflow

motifs that outline i) the kinds of data intensive activities that areobserved in workflows (data oriented motifs), and ii) the differentmanners in which activities are implemented within workflows(workflow oriented motifs). These motifs can be useful to informworkflow designers on the good and bad practices for workflowdevelopment, to inform the design of automated tools for thegeneration of workflow abstractions, etc.

I. INTRODUCTION

Scientific workflows have been increasingly used in the lastdecade as an instrument for data intensive scientific analysis.In these settings, workflows serve a dual function: first asdetailed documentation of the method (i. e. the input sourcesand processing steps taken for the derivation of a certaindata item) and second as re-usable, executable artifacts fordata-intensive analysis. Workflows stitch together a varietyof data manipulation activities such as data movement, datatransformation or data visualization to serve the goals of thescientific study. The stitching is realized by the constructsmade available by the workflow system used and is largelyshaped by the environment in which the system operates andthe function undertaken by the workflow.

A variety of workflow systems are in use [10] [3] [7] [2]serving several scientific disciplines. A workflow is a softwareartifact, and as such once developed and tested, it can beshared and exchanged between scientists. Other scientists canthen reuse existing workflows in their experiments, e.g., assub-workflows [17]. Workflow reuse presents several advan-tages [4]. For example, it enables proper data citation andimproves quality through shared workflow development byleveraging the expertise of previous users. Users can alsore-purpose existing workflows to adapt them to their needs[4]. Emerging workflow repositories such as myExperiment

[14] and CrowdLabs [8] have made publishing and findingworkflows easier, but scientists still face the challenges of re-use, which amounts to fully understanding and exploiting theavailable workflows/fragments. One difficulty in understandingworkflows is their complex nature. A workflow may containseveral scientifically-significant analysis steps, combined withvarious other data preparation activities, and in differentimplementation styles depending on the environment andcontext in which the workflow is executed. The difficulty inunderstanding causes workflow developers to revert to startingfrom scratch rather than re-using existing fragments.

Through an analysis of the current practices in scientificworkflow development, we could gain insights on the creationof understandable and more effectively re-usable workflows.Specifically, we propose an analysis with the following objec-tives:

1) To reverse-engineer the set of current practices in work-flow development through an analysis of empirical evi-dence.

2) To identify workflow abstractions that would facilitateunderstandability and therefore effective re-use.

3) To detect potential information sources and heuristicsthat can be used to inform the development of tools forcreating workflow abstractions.

In this paper we present the result of an empirical analysisperformed over 177 workflow descriptions from Taverna [10]and Wings [3]. Based on this analysis, we propose a catalogueof scientific workflow motifs. Motifs are provided through i)a characterization of the kinds of data-oriented activities thatare carried out within workflows, which we refer to as data-oriented motifs, and ii) a characterization of the different man-ners in which those activity motifs are realized/implementedwithin workflows, which we refer to as workflow-orientedmotifs. It is worth mentioning that, although important, motifsthat have to do with scheduling and mapping of workflowsonto distributed resources [12] are out the scope of this paper.

The paper is structured as follows. We begin by providingrelated work in Section II, which is followed in Section III bybrief background information on Scientific Workflows, and thetwo systems that were subject to our analysis. Afterwards wedescribe the dataset and the general approach of our analysis.We present the detected scientific workflow motifs in SectionIV and we highlight the main features of their distribution

Fig. 3. Distribution of Data-Oriented Motifs per domain

Fig. 4. Distribution of Data Preparation motifs per domain

databases and shipping data to necessary locations for analysis.The impact of the environmental difference of Wings and

Taverna on the workflows is also observed in the workflow-oriented motifs (Figure 7). Stateful invocations motifs are notpresent in Wings workflows, as all steps are handled by adedicated workflow scheduling framework and the details arehidden from the workflow developers. In Taverna, the work-flow developer is responsible for catering for various differentinvocation requirements of 3rd party services, which mayinclude stateful invocations requiring execution of multipleconsecutive steps in order to undertake a single function.

Regarding workflow-oriented motifs, Figure 8 shows thatHuman-interaction steps are increasingly used in scientificworkflows, especially in the Biodiversity and Cheminformat-ics domains. Human interactions in Taverna workflows arehandled either through external tools (e.g., Google Refine),facilitated via a human-interaction plug-in, or through simplelocal scripts (e.g., selection of configuration values frommulti-choice lists). We have observed that non-trivial humaninteractions involving external tooling require a large numberof workflow steps dedicated to deploying or configuring theexternal tools, resulting in very large and complex workflows.Wings workflows do not support human interaction steps.

Finally, the large proportion of the combination of Compos-ite Workflows and Atomic Workflows motif in Figure 8 shows

Fig. 5. Data Preparation Motifs in the Genomics Workflows

Fig. 6. Data-Oriented Motifs in the Genomics Workflows

that the use of sub-workflows is an established best practicefor modularizing functionality.

VI. DISCUSSION

Our analysis shows that the nature of the environment inwhich a workflow system operates can bring-about obstaclesagainst the re-usability of workflows.

A. Obfuscation of Scientific WorkflowsData-intensive scientific analysis could be large and com-

plex with several processing steps corresponding to differentphases of data analysis performed over various kinds of data.This complexity is exacerbated when the workflow operates inan open environment, like Taverna’s, and composes multiplethird party services supporting different data formats andprotocols. In such cases the workflow contains additional stepsfor coping with different format and protocol requirements.This obfuscation of the workflow burdens the documentationfunction and creates difficulty for the workflow re-user sci-entists, who seeks to have a complete understanding of thefunction and the details of the workflow that they are re-usingin order to be able make scientific claims with their workflowbased studies.

Obfuscation is caused by the abundance of data preparationsteps, data movement operations and multi-step stateful invo-cations. One way to overcome obfuscation is to encapsulate

Data-Oriented Motifs per Domain

Data is the BottleneckCommon Motifs in Scientific Workflows:

An Empirical AnalysisDaniel Garijo⇤, Pinar Alper †, Khalid Belhajjame†, Oscar Corcho⇤, Yolanda Gil‡, Carole Goble†

⇤Ontology Engineering Group, Universidad Politecnica de Madrid. {dgarijo, ocorcho}@fi.upm.es†School of Computer Science, University of Manchester. {alperp, khalidb, carole.goble}@cs.manchester.ac.uk

‡Information Sciences Institute, Department of Computer Science, University of Southern California. gil@isi.edu

Abstract—While workflow technology has gained momentumin the last decade as a means for specifying and enacting compu-tational experiments in modern science, reusing and repurposingexisting workflows to build new scientific experiments is still adaunting task. This is partly due to the difficulty that scientistsexperience when attempting to understand existing workflows,which contain several data preparation and adaptation steps inaddition to the scientifically significant analysis steps. One wayto tackle the understandability problem is through providingabstractions that give a high-level view of activities undertakenwithin workflows. As a first step towards abstractions, we reportin this paper on the results of a manual analysis performed overa set of real-world scientific workflows from Taverna and Wingssystems. Our analysis has resulted in a set of scientific workflow

motifs that outline i) the kinds of data intensive activities that areobserved in workflows (data oriented motifs), and ii) the differentmanners in which activities are implemented within workflows(workflow oriented motifs). These motifs can be useful to informworkflow designers on the good and bad practices for workflowdevelopment, to inform the design of automated tools for thegeneration of workflow abstractions, etc.

I. INTRODUCTION

Scientific workflows have been increasingly used in the lastdecade as an instrument for data intensive scientific analysis.In these settings, workflows serve a dual function: first asdetailed documentation of the method (i. e. the input sourcesand processing steps taken for the derivation of a certaindata item) and second as re-usable, executable artifacts fordata-intensive analysis. Workflows stitch together a varietyof data manipulation activities such as data movement, datatransformation or data visualization to serve the goals of thescientific study. The stitching is realized by the constructsmade available by the workflow system used and is largelyshaped by the environment in which the system operates andthe function undertaken by the workflow.

A variety of workflow systems are in use [10] [3] [7] [2]serving several scientific disciplines. A workflow is a softwareartifact, and as such once developed and tested, it can beshared and exchanged between scientists. Other scientists canthen reuse existing workflows in their experiments, e.g., assub-workflows [17]. Workflow reuse presents several advan-tages [4]. For example, it enables proper data citation andimproves quality through shared workflow development byleveraging the expertise of previous users. Users can alsore-purpose existing workflows to adapt them to their needs[4]. Emerging workflow repositories such as myExperiment

[14] and CrowdLabs [8] have made publishing and findingworkflows easier, but scientists still face the challenges of re-use, which amounts to fully understanding and exploiting theavailable workflows/fragments. One difficulty in understandingworkflows is their complex nature. A workflow may containseveral scientifically-significant analysis steps, combined withvarious other data preparation activities, and in differentimplementation styles depending on the environment andcontext in which the workflow is executed. The difficulty inunderstanding causes workflow developers to revert to startingfrom scratch rather than re-using existing fragments.

Through an analysis of the current practices in scientificworkflow development, we could gain insights on the creationof understandable and more effectively re-usable workflows.Specifically, we propose an analysis with the following objec-tives:

1) To reverse-engineer the set of current practices in work-flow development through an analysis of empirical evi-dence.

2) To identify workflow abstractions that would facilitateunderstandability and therefore effective re-use.

3) To detect potential information sources and heuristicsthat can be used to inform the development of tools forcreating workflow abstractions.

In this paper we present the result of an empirical analysisperformed over 177 workflow descriptions from Taverna [10]and Wings [3]. Based on this analysis, we propose a catalogueof scientific workflow motifs. Motifs are provided through i)a characterization of the kinds of data-oriented activities thatare carried out within workflows, which we refer to as data-oriented motifs, and ii) a characterization of the different man-ners in which those activity motifs are realized/implementedwithin workflows, which we refer to as workflow-orientedmotifs. It is worth mentioning that, although important, motifsthat have to do with scheduling and mapping of workflowsonto distributed resources [12] are out the scope of this paper.

The paper is structured as follows. We begin by providingrelated work in Section II, which is followed in Section III bybrief background information on Scientific Workflows, and thetwo systems that were subject to our analysis. Afterwards wedescribe the dataset and the general approach of our analysis.We present the detected scientific workflow motifs in SectionIV and we highlight the main features of their distribution

Fig. 3. Distribution of Data-Oriented Motifs per domain

Fig. 4. Distribution of Data Preparation motifs per domain

databases and shipping data to necessary locations for analysis.The impact of the environmental difference of Wings and

Taverna on the workflows is also observed in the workflow-oriented motifs (Figure 7). Stateful invocations motifs are notpresent in Wings workflows, as all steps are handled by adedicated workflow scheduling framework and the details arehidden from the workflow developers. In Taverna, the work-flow developer is responsible for catering for various differentinvocation requirements of 3rd party services, which mayinclude stateful invocations requiring execution of multipleconsecutive steps in order to undertake a single function.

Regarding workflow-oriented motifs, Figure 8 shows thatHuman-interaction steps are increasingly used in scientificworkflows, especially in the Biodiversity and Cheminformat-ics domains. Human interactions in Taverna workflows arehandled either through external tools (e.g., Google Refine),facilitated via a human-interaction plug-in, or through simplelocal scripts (e.g., selection of configuration values frommulti-choice lists). We have observed that non-trivial humaninteractions involving external tooling require a large numberof workflow steps dedicated to deploying or configuring theexternal tools, resulting in very large and complex workflows.Wings workflows do not support human interaction steps.

Finally, the large proportion of the combination of Compos-ite Workflows and Atomic Workflows motif in Figure 8 shows

Fig. 5. Data Preparation Motifs in the Genomics Workflows

Fig. 6. Data-Oriented Motifs in the Genomics Workflows

that the use of sub-workflows is an established best practicefor modularizing functionality.

VI. DISCUSSION

Our analysis shows that the nature of the environment inwhich a workflow system operates can bring-about obstaclesagainst the re-usability of workflows.

A. Obfuscation of Scientific WorkflowsData-intensive scientific analysis could be large and com-

plex with several processing steps corresponding to differentphases of data analysis performed over various kinds of data.This complexity is exacerbated when the workflow operates inan open environment, like Taverna’s, and composes multiplethird party services supporting different data formats andprotocols. In such cases the workflow contains additional stepsfor coping with different format and protocol requirements.This obfuscation of the workflow burdens the documentationfunction and creates difficulty for the workflow re-user sci-entists, who seeks to have a complete understanding of thefunction and the details of the workflow that they are re-usingin order to be able make scientific claims with their workflowbased studies.

Obfuscation is caused by the abundance of data preparationsteps, data movement operations and multi-step stateful invo-cations. One way to overcome obfuscation is to encapsulate

Data-Oriented Motifs per DomainFig. 3. Distribution of Data-Oriented Motifs per domain

Fig. 4. Distribution of Data Preparation motifs per domain

databases and shipping data to necessary locations for analysis.The impact of the environmental difference of Wings and

Taverna on the workflows is also observed in the workflow-oriented motifs (Figure 7). Stateful invocations motifs are notpresent in Wings workflows, as all steps are handled by adedicated workflow scheduling framework and the details arehidden from the workflow developers. In Taverna, the work-flow developer is responsible for catering for various differentinvocation requirements of 3rd party services, which mayinclude stateful invocations requiring execution of multipleconsecutive steps in order to undertake a single function.

Regarding workflow-oriented motifs, Figure 8 shows thatHuman-interaction steps are increasingly used in scientificworkflows, especially in the Biodiversity and Cheminformat-ics domains. Human interactions in Taverna workflows arehandled either through external tools (e.g., Google Refine),facilitated via a human-interaction plug-in, or through simplelocal scripts (e.g., selection of configuration values frommulti-choice lists). We have observed that non-trivial humaninteractions involving external tooling require a large numberof workflow steps dedicated to deploying or configuring theexternal tools, resulting in very large and complex workflows.Wings workflows do not support human interaction steps.

Finally, the large proportion of the combination of Compos-ite Workflows and Atomic Workflows motif in Figure 8 shows

Fig. 5. Data Preparation Motifs in the Genomics Workflows

Fig. 6. Data-Oriented Motifs in the Genomics Workflows

that the use of sub-workflows is an established best practicefor modularizing functionality.

VI. DISCUSSION

Our analysis shows that the nature of the environment inwhich a workflow system operates can bring-about obstaclesagainst the re-usability of workflows.

A. Obfuscation of Scientific WorkflowsData-intensive scientific analysis could be large and com-

plex with several processing steps corresponding to differentphases of data analysis performed over various kinds of data.This complexity is exacerbated when the workflow operates inan open environment, like Taverna’s, and composes multiplethird party services supporting different data formats andprotocols. In such cases the workflow contains additional stepsfor coping with different format and protocol requirements.This obfuscation of the workflow burdens the documentationfunction and creates difficulty for the workflow re-user sci-entists, who seeks to have a complete understanding of thefunction and the details of the workflow that they are re-usingin order to be able make scientific claims with their workflowbased studies.

Obfuscation is caused by the abundance of data preparationsteps, data movement operations and multi-step stateful invo-cations. One way to overcome obfuscation is to encapsulate

Data-Preparation Motifs per Domain

Make Data FlourishFrom data to information to knowledge

Make Data Flourish

Papers explicitly link to data

Track and publish explicit provenance information

Capture the processes by which data is manipulated

Global identification of data sets and data items

Metadata expressed usingshared vocabularies

From data to information to knowledge

Data uses a common syntax

Make Data Flourish

Papers explicitly link to data

Track and publish explicit provenance information

Capture the processes by which data is manipulated

Global identification of data sets and data items

Metadata expressed usingshared vocabularies

From data to information to knowledge

Data uses a common syntax"Someone who is not the person who collected the data can understand the experiment and data" - Shreejoy Tripathy

Linked Data• Use existing Web infrastructure

• Everything gets a URI and usually a category

• Express typed relations between things (triples)

• Express sameness or difference

• Reuse identifiers as much as possible

+ =

Salah, Alkim Almila Akdag, Cheng Gao, Krzysztof Suchecki, and Andrea Scharnhorst. 2012. “Need to Categorize: A Comparative Look at the Categories of Universal Decimal Classification System and Wikipedia.” Leonardo 45 (1) (February): 84-85. doi:10.1162/LEON_a_00344. (Preprint http://arxiv.org/abs/1105.5912v1)

Linked Data for ScienceNeuroscience Information Framework

(Ontologies, Semantic Wiki, Catalog)

BioPortal (ontologies)

Workflow Systems (WINGS, Taverna, …)

Rightfield (systems biology)

Organic Data Publishing (Semantic Wiki)

Linked Science (tools)

Nanopublications (small scientific assertions)

Bio2RDF (big linked data)

…Claire Monteleoni

As of September 2011

MusicBrainz

(zitgist)

P20

Turismo de

Zaragoza

yovisto

Yahoo! Geo

Planet

YAGO

World Fact-book

El ViajeroTourism

WordNet (W3C)

WordNet (VUA)

VIVO UF

VIVO Indiana

VIVO Cornell

VIAF

URIBurner

Sussex Reading

Lists

Plymouth Reading

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UniRef

UniProt

UMBEL

UK Post-codes

legislationdata.gov.uk

Uberblic

UB Mann-heim

TWC LOGD

Twarql

transportdata.gov.

uk

Traffic Scotland

theses.fr

Thesau-rus W

totl.net

Tele-graphis

TCMGeneDIT

TaxonConcept

Open Library (Talis)

tags2con delicious

t4gminfo

Swedish Open

Cultural Heritage

Surge Radio

Sudoc

STW

RAMEAU SH

statisticsdata.gov.

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St. Andrews Resource

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ECS South-ampton EPrints

SSW Thesaur

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SmartLink

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semanticweb.org

SemanticTweet

Semantic XBRL

SWDog Food

Source Code Ecosystem Linked Data

US SEC (rdfabout)

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graphy

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RISKS

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NSF

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JISC

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IEEE

IBM

Eurécom

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DBLP (RKB

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Crime Reports

UK

Course-ware

CORDIS (RKB

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Budapest

ACM

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Revyu

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Poké-pédia

patentsdata.go

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Ord-nance Survey

Openly Local

Open Library

OpenCyc

Open Corpo-rates

OpenCalais

OpenEI

Open Election

Data Project

OpenData

Thesau-rus

Ontos News Portal

OGOLOD

JanusAMP

Ocean Drilling Codices

New York

Times

NVD

ntnusc

NTU Resource

Lists

Norwe-gian

MeSH

NDL subjects

ndlna

myExperi-ment

Italian Museums

medu-cator

MARC Codes List

Man-chester Reading

Lists

Lotico

Weather Stations

London Gazette

LOIUS

Linked Open Colors

lobidResources

lobidOrgani-sations

LEM

LinkedMDB

LinkedLCCN

LinkedGeoData

LinkedCT

LinkedUser

FeedbackLOV

Linked Open

Numbers

LODE

Eurostat (OntologyCentral)

Linked EDGAR

(OntologyCentral)

Linked Crunch-

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lingvoj

Lichfield Spen-ding

LIBRIS

Lexvo

LCSH

DBLP (L3S)

Linked Sensor Data (Kno.e.sis)

Klapp-stuhl-club

Good-win

Family

National Radio-activity

JP

Jamendo (DBtune)

Italian public

schools

ISTAT Immi-gration

iServe

IdRef Sudoc

NSZL Catalog

Hellenic PD

Hellenic FBD

PiedmontAccomo-dations

GovTrack

GovWILD

GoogleArt

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gnoss

GESIS

GeoWordNet

GeoSpecies

GeoNames

GeoLinkedData

GEMET

GTAA

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MediCare

Euro-stat

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EURES

DrugBank

Disea-some

DBLP (FU

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DailyMed

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Freebase

flickr wrappr

Fishes of Texas

Finnish Munici-palities

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FanHubz

EventMedia

EUTC Produc-

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Eurostat

Europeana

EUNIS

EU Insti-

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ESD stan-dards

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NHS(En-

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Energy (En-

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Crime(En-

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DDC Deutsche Bio-

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John Peel

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DBTropes

Portu-guese

DBpedia

dbpedia lite

Greek DBpedia

DBpedia

data-open-ac-uk

SMCJournals

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NASA (Data Incu-bator)

MusicBrainz(Data

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Moseley Folk

Metoffice Weather Forecasts

Discogs (Data

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Climbing

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Data Gov.ie

databnf.fr

Cornetto

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Chronic-ling

America

Chem2Bio2RDF

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businessdata.gov.

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Brazilian Poli-

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UniSTS

UniPathway

UniParc

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UniProt(Bio2RDF)

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Reactome

PubMedPub

Chem

PRO-SITE

ProDom

Pfam

PDB

OMIMMGI

KEGG Reaction

KEGG Pathway

KEGG Glycan

KEGG Enzyme

KEGG Drug

KEGG Com-pound

InterPro

HomoloGene

HGNC

Gene Ontology

GeneID

Affy-metrix

bible ontology

BibBase

FTS

BBC Wildlife Finder

BBC Program

mes BBC Music

Alpine Ski

Austria

LOCAH

Amster-dam

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AGROVOC

AEMET

US Census (rdfabout)

Media

Geographic

Publications

Government

Cross-domain

Life sciences

User-generated content

Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

As of September 2011

MusicBrainz

(zitgist)

P20

Turismo de

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yovisto

Yahoo! Geo

Planet

YAGO

World Fact-book

El ViajeroTourism

WordNet (W3C)

WordNet (VUA)

VIVO UF

VIVO Indiana

VIVO Cornell

VIAF

URIBurner

Sussex Reading

Lists

Plymouth Reading

Lists

UniRef

UniProt

UMBEL

UK Post-codes

legislationdata.gov.uk

Uberblic

UB Mann-heim

TWC LOGD

Twarql

transportdata.gov.

uk

Traffic Scotland

theses.fr

Thesau-rus W

totl.net

Tele-graphis

TCMGeneDIT

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tags2con delicious

t4gminfo

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Surge Radio

Sudoc

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statisticsdata.gov.

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St. Andrews Resource

Lists

ECS South-ampton EPrints

SSW Thesaur

us

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semanticweb.org

SemanticTweet

Semantic XBRL

SWDog Food

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US SEC (rdfabout)

Sears

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graphy

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Roma

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RESEX

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Pisa

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JISC

IRIT

IEEE

IBM

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ERA

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DEPLOY

DBLP (RKB

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Crime Reports

UK

Course-ware

CORDIS (RKB

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Budapest

ACM

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Revyu

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tors

referencedata.gov.

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nl

RDFohloh

Last.FM (rdfize)

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Mashup

Rådata nå!

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Product Types

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Poké-pédia

patentsdata.go

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OxPoints

Ord-nance Survey

Openly Local

Open Library

OpenCyc

Open Corpo-rates

OpenCalais

OpenEI

Open Election

Data Project

OpenData

Thesau-rus

Ontos News Portal

OGOLOD

JanusAMP

Ocean Drilling Codices

New York

Times

NVD

ntnusc

NTU Resource

Lists

Norwe-gian

MeSH

NDL subjects

ndlna

myExperi-ment

Italian Museums

medu-cator

MARC Codes List

Man-chester Reading

Lists

Lotico

Weather Stations

London Gazette

LOIUS

Linked Open Colors

lobidResources

lobidOrgani-sations

LEM

LinkedMDB

LinkedLCCN

LinkedGeoData

LinkedCT

LinkedUser

FeedbackLOV

Linked Open

Numbers

LODE

Eurostat (OntologyCentral)

Linked EDGAR

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Linked Crunch-

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lingvoj

Lichfield Spen-ding

LIBRIS

Lexvo

LCSH

DBLP (L3S)

Linked Sensor Data (Kno.e.sis)

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Family

National Radio-activity

JP

Jamendo (DBtune)

Italian public

schools

ISTAT Immi-gration

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IdRef Sudoc

NSZL Catalog

Hellenic PD

Hellenic FBD

PiedmontAccomo-dations

GovTrack

GovWILD

GoogleArt

wrapper

gnoss

GESIS

GeoWordNet

GeoSpecies

GeoNames

GeoLinkedData

GEMET

GTAA

STITCH

SIDER

Project Guten-berg

MediCare

Euro-stat

(FUB)

EURES

DrugBank

Disea-some

DBLP (FU

Berlin)

DailyMed

CORDIS(FUB)

Freebase

flickr wrappr

Fishes of Texas

Finnish Munici-palities

ChEMBL

FanHubz

EventMedia

EUTC Produc-

tions

Eurostat

Europeana

EUNIS

EU Insti-

tutions

ESD stan-dards

EARTh

Enipedia

Popula-tion (En-AKTing)

NHS(En-

AKTing) Mortality(En-

AKTing)

Energy (En-

AKTing)

Crime(En-

AKTing)

CO2 Emission

(En-AKTing)

EEA

SISVU

education.data.g

ov.uk

ECS South-ampton

ECCO-TCP

GND

Didactalia

DDC Deutsche Bio-

graphie

datadcs

MusicBrainz

(DBTune)

Magna-tune

John Peel

(DBTune)

Classical (DB

Tune)

AudioScrobbler (DBTune)

Last.FM artists

(DBTune)

DBTropes

Portu-guese

DBpedia

dbpedia lite

Greek DBpedia

DBpedia

data-open-ac-uk

SMCJournals

Pokedex

Airports

NASA (Data Incu-bator)

MusicBrainz(Data

Incubator)

Moseley Folk

Metoffice Weather Forecasts

Discogs (Data

Incubator)

Climbing

data.gov.uk intervals

Data Gov.ie

databnf.fr

Cornetto

reegle

Chronic-ling

America

Chem2Bio2RDF

Calames

businessdata.gov.

uk

Bricklink

Brazilian Poli-

ticians

BNB

UniSTS

UniPathway

UniParc

Taxonomy

UniProt(Bio2RDF)

SGD

Reactome

PubMedPub

Chem

PRO-SITE

ProDom

Pfam

PDB

OMIMMGI

KEGG Reaction

KEGG Pathway

KEGG Glycan

KEGG Enzyme

KEGG Drug

KEGG Com-pound

InterPro

HomoloGene

HGNC

Gene Ontology

GeneID

Affy-metrix

bible ontology

BibBase

FTS

BBC Wildlife Finder

BBC Program

mes BBC Music

Alpine Ski

Austria

LOCAH

Amster-dam

Museum

AGROVOC

AEMET

US Census (rdfabout)

Media

Geographic

Publications

Government

Cross-domain

Life sciences

User-generated content

Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

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62.224.812.703 Triples!

62.224.812.703 Triples!(1.75 Billion)

LODStats Analysis

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45%

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Not RDFConnection resetUnknown responseXMLNo URL providedOtherHTTP

Hoekstra, Rinke; Groth, Paul (2013): Distribution of Errors Reported by LOD2 LODStats Project. figshare. http://dx.doi.org/10.6084/m9.figshare.695949

http://stats.lod2.eu

299 out of 639 datasets have errors

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura a Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam, The Netherlands

Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dy-namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex moni-toring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains.

Keywords: ambient agent model, human behaviour, dynamics

*Corresponding author. E-mail: t.bosse@vu.nl.

Journal of Ambient Intelligence and Smart Environments

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura a Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam, The Netherlands

Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dy-namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex moni-toring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains.

Keywords: ambient agent model, human behaviour, dynamics

*Corresponding author. E-mail: t.bosse@vu.nl.

Journal of Ambient Intelligence and Smart Environments

“Whoah! Cool, you should publish that stuff as Linked Data”

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura a Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam, The Netherlands

Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dy-namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex moni-toring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains.

Keywords: ambient agent model, human behaviour, dynamics

*Corresponding author. E-mail: t.bosse@vu.nl.

Journal of Ambient Intelligence and Smart Environments

“Whoah! Cool, you should publish that stuff as Linked Data”

“Um, but doesn’t TTL have incompatible semantics?”

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura a Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam, The Netherlands

Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dy-namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex moni-toring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains.

Keywords: ambient agent model, human behaviour, dynamics

*Corresponding author. E-mail: t.bosse@vu.nl.

Journal of Ambient Intelligence and Smart Environments

“Whoah! Cool, you should publish that stuff as Linked Data”

“Um, but doesn’t TTL have incompatible semantics?”

“Nah, silly, who cares? We’ll just start a new W3C WG!”

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura a Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam, The Netherlands

Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dy-namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex moni-toring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains.

Keywords: ambient agent model, human behaviour, dynamics

*Corresponding author. E-mail: t.bosse@vu.nl.

Journal of Ambient Intelligence and Smart Environments

“Whoah! Cool, you should publish that stuff as Linked Data”

“Um, but doesn’t TTL have incompatible semantics?”

“Nah, silly, who cares? We’ll just start a new W3C WG!”

“Uh, ok, if we must. But even then, we can’t just publish the model as is!”

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura a Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam, The Netherlands

Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dy-namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex moni-toring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains.

Keywords: ambient agent model, human behaviour, dynamics

*Corresponding author. E-mail: t.bosse@vu.nl.

Journal of Ambient Intelligence and Smart Environments

“Whoah! Cool, you should publish that stuff as Linked Data”

“Um, but doesn’t TTL have incompatible semantics?”

“Nah, silly, who cares? We’ll just start a new W3C WG!”

“Uh, ok, if we must. But even then, we can’t just publish the model as is!”

“”No worries, just add the provenance using PROV-O, annotate the PDF with OA, and link to other research using CITO.”

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura a Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam, The Netherlands

Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dy-namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex moni-toring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains.

Keywords: ambient agent model, human behaviour, dynamics

*Corresponding author. E-mail: t.bosse@vu.nl.

Journal of Ambient Intelligence and Smart Environments

“Whoah! Cool, you should publish that stuff as Linked Data”

“Um, but doesn’t TTL have incompatible semantics?”

“Nah, silly, who cares? We’ll just start a new W3C WG!”

“Uh, ok, if we must. But even then, we can’t just publish the model as is!”

“”No worries, just add the provenance using PROV-O, annotate the PDF with OA, and link to other research using CITO.”

“And that’s it?”

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura a Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam, The Netherlands

Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dy-namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex moni-toring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains.

Keywords: ambient agent model, human behaviour, dynamics

*Corresponding author. E-mail: t.bosse@vu.nl.

Journal of Ambient Intelligence and Smart Environments

“Whoah! Cool, you should publish that stuff as Linked Data”

“Um, but doesn’t TTL have incompatible semantics?”

“Nah, silly, who cares? We’ll just start a new W3C WG!”

“Uh, ok, if we must. But even then, we can’t just publish the model as is!”

“”No worries, just add the provenance using PROV-O, annotate the PDF with OA, and link to other research using CITO.”

“And that’s it?”“Noo! You’ll need persistent Cool URI’s and publish your endpoint

for eternity of course. Duh.”

An Ambient Agent Model for Monitoring and Analysing Dynamics of Complex Human Behaviour Tibor Bossea*, Mark Hoogendoorna, Michel C.A. Kleina, and Jan Treura a Vrije Universiteit Amsterdam, Department of Artificial Intelligence, de Boelelaan 1081, 1081 HV Amsterdam, The Netherlands

Abstract. In ambient intelligent systems, monitoring of a human could consist of more complex tasks than merely identifying whether a certain value of a sensor is above a certain threshold. Instead, such tasks may involve monitoring of complex dy-namic interactions between human and environment. In order to enable such more complex types of monitoring, this paper presents a generic agent-based framework. The framework consists of support on various levels of system design, namely: (1) the top level, including the interaction between agents, (2) the agent level, providing support on the design of individual agents, and (3) the level of monitoring complex dynamic behaviour, allowing the specification of the aforementioned complex moni-toring properties within the agents. The approach is exemplified by a large case study concerning the assessment of driving behaviour, and is applied to two smaller cases as well (concerning fall detection of elderly, and assistance of naval operations, respectively), which are briefly described. These case studies have illustrated that the presented framework enables developers within ambient intelligence to build systems with more expressiveness regarding their monitoring focus. Moreover, they have shown that the framework is easy to use and applicable in a wide variety of domains.

Keywords: ambient agent model, human behaviour, dynamics

*Corresponding author. E-mail: t.bosse@vu.nl.

Journal of Ambient Intelligence and Smart Environments

“Whoah! Cool, you should publish that stuff as Linked Data”

“Um, but doesn’t TTL have incompatible semantics?”

“Nah, silly, who cares? We’ll just start a new W3C WG!”

“Uh, ok, if we must. But even then, we can’t just publish the model as is!”

“”No worries, just add the provenance using PROV-O, annotate the PDF with OA, and link to other research using CITO.”

“And that’s it?”“Noo! You’ll need persistent Cool URI’s and publish your endpoint

for eternity of course. Duh.”“Eh?”

“Oh... and don’t forget all data collected by the agents, in all runs, including the first experiments. Now THAT would be ultra cool.

“Ngh!?”

Creating Linked Data• Decide on resources to describe

• Mint cool URIs

• Decide on triples to include

• Describe the dataset

• Choose vocabularies

• Define terms

• Make links

• Publish to triple store/annotations/dump

http://linkeddatabook.com

If this already is tedious...

... can you expect researchers to publish Linked Research Data?

If this already is tedious...

... can you expect researchers to publish Linked Research Data?

Conclusion?

Linked Data is sóóóóó 2005

We need to make publishing Linked Research Data...

... more persistent ... ... and more rewarding....a lot easier...

We need to make publishing Linked Research Data...

... more persistent ... ... and more rewarding....a lot easier...

“People as frontier in computing” - Haym Hirsch, Pietro Michelucci

http://linkitup.data2semantics.org

We need to make publishing Linked Research Data...

... more persistent ... ... and more rewarding....a lot easier...

• Lightweight web application

• Interface to API of existing data repositories

• Enrich metadata by linking to (linked) data resources

• Human in the Loop

• Track provenance

• Publish rich metadata as new data publication

http://linkitup.data2semantics.org

We need to make publishing Linked Research Data...

... more persistent ... ... and more rewarding....a lot easier...

Nanopublication + OA + PROV-O + DCTerms + FOAF

• Lightweight web application

• Interface to API of existing data repositories

• Enrich metadata by linking to (linked) data resources

• Human in the Loop

• Track provenance

• Publish rich metadata as new data publication

http://linkitup.data2semantics.org

We need to make publishing Linked Research Data...

... more persistent ... ... and more rewarding....a lot easier...

Nanopublication + OA + PROV-O + DCTerms + FOAF

Use tags & categories to query the DBpedia endpoint

Use authors to query the DBLP endpoint

Use tags & categories to query the NeuroLex endpoint

Use author names to query the ORCID API

Extract references to resolve to CrossRef DOIs

Every operation is tracked automatically

Connection to PROV-O-Viz service

http://semweb.cs.vu.nl/provoviz

Review selected links, and publish to Figshare

PluginsName Service Source Links toDBLP SPARQL Authors Author IdentifiersORCID REST Authors Author Identifiers

LinkedLifeData REST Tags & Categories Biomedical EntitiesCrossref Custom Citations DOIs

Elsevier LDR REST Tags & Categories Funding agenciesDANS EASY Custom Tags & Categories General Datasets

SameAs REST Links General EntitiesDBPedia Spotlight REST Description, Tags &

CategoriesGeneral Entities

DBPedia/Wikipedia SPARQL Tags & Categories General EntitiesNeuroLex SPARQL Tags & Categories Neuroscience Concepts

NIF Registry REST Tags & Categories Neuroscience Datasetsyour data set here

What does this solve?• Decide on resources to describe

• Mint cool URIs

• Decide on triples to include

• Describe the dataset

• Choose vocabularies

• Define terms

• Make links

• Publish to triple store/annotations/dump

http://linkeddatabook.com

What does this solve?• You decide on resources to describe

• We mint cool URIs

• We decide on triples to include

• We describe the dataset

• We choose vocabularies

• We define terms

• Together we make links

• We publish the dataset to a reliable repository

http://linkeddatabook.com

Coming up…• Publish directly from Dropbox, Github, …

• Reconstruct provenance information (http://git2prov.org)

• Analyze, convert and enrich on the fly

• Generate a data report for advertisement purposes

• Measure for information content of datasets (“D-Index”)

• Integrate a data dashboard

linkitup … enhancing the data publication…

… increasing findability …

… boosting reusability …

… result is stored persistently

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302212116http://linkitup.data2semantics.org

http://www.data2semantics.org

http://yasgui.data2semantics.org http://semweb.cs.vu.nl/provoviz

http://git2prov.org