The Information Systems Group at HPI

Felix Naumann and Ralf KrestelHasso Plattner Institute

Potsdam, Germanyfirstname.lastname@hpi.de

ABSTRACTThe Hasso Plattner Institute (HPI) is a private com-puter science institute funded by the eponymous SAPco-founder. It is affiliated with the University of Pots-dam in Germany and is dedicated to research and teach-ing, awarding B.Sc., M.Sc., and Ph.D. degrees.

The Information Systems group was founded in 2006,currently has around ten Ph.D. students and about 15masters students actively involved in our research ac-tivities. Our initial and still ongoing research focus hasbeen the area of data cleansing and duplicate detection.More recently we have become active in the area of textmining to extract structured information from text, andeven more recently in data profiling, i.e., the task ofdiscovering various metadata and dependencies from adata instance.

1. MOTIVATIONData abounds – it appears in many forms rang-

ing from traditional relational or XML databasesover semi-structured data, often published as linkedopen data, to textual data from documents on theWeb. This wealth of data is ever growing, and manyorganizations and researchers have recognized thebenefit of integrating it into larger sets of homoge-neous, consistent, and clean data. Integrated dataconsolidates disconnected sources in organizations;it combines experimental results to gain new sci-entific insights; it provides consumers with a morecomplete view of product offers, etc.

Yet integration of such data is difficult due toits often extreme heterogeneity: Syntactic het-erogeneity in data formats, access protocols, andquery languages is typically the most simple toovercome, usually by building appropriate source-specific wrapper components. Next, structural het-erogeneity must be overcome by aligning the dif-ferent schemata of the datasets: Schema matchingtechniques automatically detect similarity and cor-respondence among schema elements, while schemamapping techniques interpret these to actually

transform the data. Finally, to overcome semanticheterogeneity the different meanings of data and thesimilar but different representations of real-worldentities must be recognized. Here, similarity searchand data cleansing techniques are employed.

While the first two challenges have been researchtopics of our’s in the past, the last and arguablymost difficult challenge is a main focus of our cur-rent research endeavors. This focus manifests itselfin three main research directions, which are mo-tivated in the following sections: First, and mostrecently, in the area of data profiling, i.e., the de-velopment of methods to discover interesting prop-erties about unknown datasets. Second, in the areaof data cleansing, i.e., the development of methodsto automatically correct errors and inconsistenciesin databases and in particular to search and con-solidate duplicates. Third, the area of text mining,i.e., the extraction of information from textual data,such as Wikipedia articles, tweets, or other text.

Where possible we aim at making our data andour algorithms available. A good starting point tofind them is http://hpi.de/naumann/projects/

repeatability.html.

2. DATA PROFILING“Data profiling is the set of activities and pro-

cesses to determine the metadata about a givendataset.” [1] The need to profile a new or unfa-miliar data arises in many situations, in general toprepare for some subsequent task. Data profilingcomprises a broad range of methods to efficientlyanalyze a given dataset. In a typical scenario, mir-roring the capabilities of commercial data profilingtools, tables of a relational database are scannedto derive metadata including data types and typi-cal value patterns, completeness and uniqueness ofcolumns, keys and foreign keys, and occasionallyfunctional dependencies and association rules. Inaddition, research (ours and others’) has proposedmany methods for further tasks, such as the discov-

ery of inclusion dependencies or conditional func-tional dependencies. There are a number of con-crete use cases for data profiling results, including:

• Query optimization: counts and histogramsfor selectivity estimation, dependencies forquery simplification

• Data cleansing: pattern and dependency de-tection to identify violations

• Data integration: inter-database inclusion de-pendencies to enrich datasets and find join-paths

• Data analytics: data preparation and initialinsights

• Database reverse engineering: foreign key dis-covery to understand a schema and identify itscore components

Our survey [1] highlights the community’s signif-icant research progress in this area in the recentpast. Data profiling is becoming a more and morepopular topic as researchers and practitioners arerecognizing that just gathering data into data lakesis not sufficient: “If we just have a bunch of datasets in a repository, it is unlikely anyone will ever beable to find, let alone reuse, any of this data. Withadequate metadata, there is some hope [. . . ]” [4]

2.1 Profiling relational dataApart from computationally more simple tasks,

such as counting the number of distinct values in acolumn, data profiling is typically concerned withdiscovering dependencies in a given, possibly largedataset. We, and other groups, have developedvarious methods to efficiently discover all mini-mal functional dependencies, inclusion dependen-cies, unique column combinations, and order de-pendencies. More dependencies are to come, suchas join dependencies, matching dependencies, de-nial constraints, etc. Instead of listing and explain-ing each technique in any detail, we highlight somegeneral difficulties we have encountered that makedata profiling both challenging and interesting:

Schema size: Because dependencies can occuramong any column or column combination,not only the number of records, but also thenumber of columns is a decisive factor of com-plexity.

Size of dependencies: One way to handle the ex-ponential search space is to limit the size of thedependencies, i.e., the number of involved at-tributes. For instance, one could argue that

key-candidates with more than ten attributesare not useful. On the other hand, a completeset of metadata can be useful, for instance tonormalize a relation based on its functional de-pendencies.

Number of dependencies: While much depen-dency-focussed research, such as normaliza-tion theory or reasoning with dependencies,assumes a handful of dependencies as input,we typically observe thousands, millions andin some cases even billions of dependencies intypical real-world datasets. Just storing thembecomes a problem, not to mention reasoningabout them or interpreting them manually.

Treatment of nulls: The semantics of missingvalues is an interesting problem for almost anydata management and analysis task, likewisefor data profiling [13].

Intricate pruning: Huhtala et al. already showedquite complex insights to efficiently prune thesearch space for FD discovery [10]. When pro-filing for various types of dependencies, cross-dependency pruning becomes possible.

Relaxed dependencies: Apart from strict de-pendencies, it is also of interest to discoverpartial dependencies, which are true for onlya part of the dataset, and conditional depen-dencies, which are true for a well-defined suchpart.

Dynamic data: While most of our focus has beenon algorithms for a given, static dataset, weare also interested in efficiently updating dataprofiling results after changes in the data.

Experiments: Testing correctness of algorithmsfor given, real-world datasets is straightfor-ward, but generating artificial testdata withcertain properties, such as a certain numberand distribution of functional dependencies, isvery challenging.

Interpreting results: Any discovered metadatacan only be validated for the dataset at hand.Some might be true in general, some might bespurious. We discuss this arguably most im-portant and most difficult challenge of makingsense of profiling results in Section 2.4.

In conclusion, research has many avenues to fol-low!

2.2 The Metanome projectMetanome is our open Java-based framework and

tool for managing relational datasets and data pro-filing algorithms [18]. Our motivation for this un-dertaking is to bundle the many algorithms devel-oped in our group, to provide an easy interfaceand testing environment for developers of new al-gorithms, and finally to enable fair comparisonsamong competing algorithms. Our initial focus wason functional dependency discovery, and Metanomefeatures implementations of already eight publishedFD-discovery algorithms including those evaluatedin [19] plus seven further algorithms for other dis-covery tasks (www.metanome.de).

2.3 Profiling RDF dataAmong the datasets that are particularly wor-

thy to profile, due to their variety and their gen-eral interest, are linked datasets. We are apply-ing traditional and novel data mining technology tolinked data in its RDF representation as subject-predicate-object triples. For instance, the discoveryof frequent itemsets of predicates or objects in thecontext of subjects allows enriching datasets withmissing triples. Another configuration – miningfor frequent subjects in the context of predicates– achieves a clustering of entities. We have also ap-plied data mining techniques for the discovery ofconditional inclusion dependencies [16]. The vol-ume of available linked data (a popular dataset isfrom the Billion Triples Challenge, which currentlycomprises over 3 billion facts) necessitates space-efficient algorithms.

Again, much of our work enters our browser-based discovery tool, ProLOD++ [2], which fea-tures techniques to discovery key-candidates, ex-plore class and property distributed, discover fre-quent graph patterns, and more (see Figure 1).

2.4 From metadata to semanticsFinding all (and thus very many) dependencies in

a given dataset is only the first part of a meaningfuldiscovery process. The vast majority of metadatais spurious: It might be valid only in the currentinstance, or it might be valid for any reasonableinstance but meaningless nonetheless. Separatingthe wheat from the chaff is extremely difficult, asit is a jump from (meta-)data to semantics; only ahuman can promote a unique column combinationto a key, an inclusion dependency to a foreign key, ora functional dependency to an enforced constraint.

But computer science can help: We are cur-rently investing much of our time to transform largeamounts of metadata to schematic information. A

Figure 1: Exploring frequent patternsin a Linked Dataset with ProLOD(www.prolod.org)

Figure 2: Clusters of web tables, connectedthrough (reasonable) inclusion dependencies

first step is a metadata management system to storeand query many different types of metadata. Next,we are developing selection and ranking methodsto present to users only the most promising meta-data. And finally, the visualization of metadata isan important tool to aid experts in understandingtheir data. Figure 2, for instance, shows connectedcomponents created by discovering inclusion depen-dencies among millions of web tables.

3. DATA CLEANSINGWith the ever-increasing volume of data, data

quality problems arise. One of the most intriguingproblems is that of multiple, yet different represen-tations of the same real-world object in the data:duplicates. Such duplicates have many detrimentaleffects, for instance bank customers can obtain du-plicate identities, inventory levels are monitored in-

correctly, catalogs are mailed multiple times to thesame household, etc. A related problem is that ofsimilarity search in structured data: given a queryrecord, find the most similar candidate records ina database and identify whether one of them is amatch.

The areas of similarity search and duplicate de-tection are experiencing a renaissance both in re-search and industry. Apart from scientific contribu-tions we cooperate with companies to transfer ourtechnology. Both our similarity search and our du-plicate detection techniques have been adopted byindustry partners.

3.1 Duplicate detectionDetecting duplicates is difficult: First, duplicate

representations are usually not identical but slightlydiffer in their values. Second, in principle, all pairsof records should be compared, which is infeasiblefor large volumes of data [9]. Our research addressesboth aspects by designing effective similarity mea-sures and by developing efficient algorithms to re-duce the search space.

One focus of our work is to develop improved vari-ations of the elegant and simple sorted neighbor-hood method [8], for instance adapting it to nestedXML data, making it progressive, parallelizing itfor GPU-processing, or creating an adaptive versionthat is provably more efficient than the original [5].

In our experience, research(ers) in duplicate de-tection suffers particularly when trying to trans-fer technology and methods to industrial settings:Availability of data is a first issue, that arises evenif a cooperation is firmly established and all par-ticipating parties in principle agree to the effort.Next, domain- and partner-specific similarity mea-sures are needed that satisfy the specific use-case.Companies can have widely differing views of whatconstitutes a duplicate: Measuring recall is impos-sible due to large dataset sizes, and precision is sur-prisingly malleable, depending on whom one asksfor validation. And finally, the real world holdsmany nitty, gritty details that can be convenientlyignored in a research setting1. With [20] we wereable to overcome these difficulties and have had alasting impact on the data quality of our partner.

3.2 Similarity searchA problem related to duplicate detection, but of-

fering quite different requirements is that of efficientsimilarity search. Instead of comparing all or manypairs of records in an offline fashion (n × n), on-

1For instance, providing a machine with 16GB mainmemory but insisting on a 32-bit operating system.

line similarity search asks for all records matchinga given query record (1 × n). A typical use case isa call center agent pulling up customer informationbased on a customer’s name and city. The mainchallenge is to develop a suitable similarity index,a much more difficult undertaking that an exact-match index.

One of our solutions matches the problem to aquery plan optimization task, choosing similarityindex accesses based on their selectivity and theircost, each of which is again modified by the dynami-cally chosen threshold: A low thresholds yields morecandidates, but also more access to disk to retrievethe candidates [17]. A further insight is the impor-tance of frequency-aware similarity measures, whichapply different weights depending on the frequencyof the query terms (Schwarzenegger vs. Miller).

We are currently extending this work to solve theproblem of an ever-growing set of data that shall beheld duplicate free: each query can simultaneouslybe an insert-operation.

4. TEXT MININGUnstructured data in the form of textual doc-

uments can be found everywhere, from medi-cal records to game chats, and from politicians’speeches to tweets. These documents cover a vari-ety of genres, from serious to fun, from entire nov-els to single words. This diversity makes dealingwith textual data particularly challenging and thereis no one-size-fits-all text mining method yet. Weare currently working on the topics of named entitylinking, topic modeling, and bias detection on var-ious document collections from the web. We coverthe research areas natural language processing, in-formation extraction, and recommender systems.

4.1 Named entity linkingA first step in analyzing texts is to find entities.

Named entity linking is a rather new task composedof named entity recognition and linking the textualmentions in a document to corresponding entriesin a knowledge base, thus disambiguating the men-tions. The disambiguation can be performed usingadditional information in the knowledge base andthe context of the mentions in the documents. Wedeveloped a named entity linking approach that op-erates on a textual range of relevant terms. We thenaggregate decisions from an ensemble of simple clas-sifiers, each of which operates on a randomly sam-pled subset from the above range [21]. The obtainedresults are very good with respect to precision andrecall.

Some tasks, such as topic-based clustering, re-quire near perfect precision and therefore we en-hanced our named entity linking approach usingrandom walks [6]. This allows for efficient compu-tation of the linking and improves the precision ata minimal expense of recall.

4.2 Relationship extractionOnce entities are successfully extracted and dis-

ambiguated, finding relations between those entitiesis a next logical step. In the context of an industryproject with a large German bank, we aim at build-ing company networks to support their risk manage-ment department. These company networks are ex-tracted automatically from newspaper articles, pos-ing new challenges to the named entity recognitiontask, which is particularly difficult for German com-pany names, due to complex, often ambiguous nam-ing. Further, the relationship types we are inter-ested in differ from standard, binary relations, suchas “married with” or “located in”. Our companynetworks require the detection of relations that arenot necessarily binary, e.g. “competitor with” or“supplier to”. To this end, we developed a holistic,seed-based algorithm to find these types of relationsby providing a handful of example instantiations.The algorithm is based on Snowball [3] and can dealwith any type of user-provided relations to extractrelationship types with high precision.

4.3 Recommender systemsAs with the information extraction tasks, we

have a strong focus on the application of our re-search. Therefore, personalization, prediction, andrecommendation play a major role in our group’swork. From predicting accepted answers in MOOCforums [11], to recommending hashtags in Twit-ter [7], we analyzed diverse text collections acces-sible through the web. We also experimented withrecommending serendipitous news articles [12] topresent to the user not only relevant and novel ar-ticles, but also some surprising ones.

In an attempt to bridge the gap between tradi-tional news and social media, we developed a tweetrecommender system [15]. The goal was to pro-vide the reader of a news article about some eventwith an overview of the reactions in Twitter. WhileTwitter is often only used to share and distributeinformation, it is also used to express opinions, re-ject ideas, or support certain viewpoints. To de-tect these (subtle) opinions, traditional sentimentanalysis techniques have to be adapted to recognizeemojis, abbreviations, slang, etc. The mismatch be-tween the language used in news articles and tweets

makes recommending one based on the other chal-lenging.

4.4 Bias detectionFinally, we have to deal with another mismatch

between used languages when trying to detect po-litical bias of mainstream media. Initial experi-ments on comparing parliamentary speeches withnews articles of various German news outlets [14]have shown that perceived bias can be automat-ically quantified. Given the very different genres(speeches vs. articles), detecting biased statementsbased on their comparison is rather cumbersome.Only rarely vocabulary use is a good indicator (e.g.,“nuclear energy” vs. “atomic energy” in Germany).Nevertheless, identifying this bias in mainstreammedia and making it visible to the reader is an im-portant piece of information.

Beside this statement bias, newspapers can alsoinfluence their readers by only reporting about cer-tain topics (gate-keeping bias) or covering certainpositions more thoroughly than others (coveragebias). Automatically detecting all three kinds ofbias is our current goal, making it necessary to ex-tract not only entities (politicians, parties, domainexperts) and their relations, but also to do fine-grained opinion mining and sentiment analysis.

5. ACKNOWLEDGMENTS.Our research has been funded by various partners

including the DFG and companies interested in un-derstanding and improving their data. We are verygrateful for being able to work with our great Ph.D.students, who currently are Toni Gruetze, HazarHarmouch, Maximilian Jenders, Anja Jentzsch,John Koumarelas, Sebastian Kruse, KonstantinaLazaridou, Michael Loster, Thorsten Papenbrock,Ahmad Samiei, and Zhe Zuo.

Two further groups at HPI, with which we col-laborate, are based in the database community:The Enterprise Platforms and Integration Con-cepts (EPIC) group headed by Hasso Plattner andMatthias Uflacker, and the Knowledge Discoveryand Data Mining (KDD) group headed by Em-manuel Muller.

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