This is an old revision of the document!
The primary area of research in the Web and Information Systems laboratory of the the Department of Computer & Decision Engineering concerns information systems (both traditional and on the web). Broadly speaking, we can identify the following major themes in the laboratory's research. The MFE subjects presented below cover these themes.
Our laboratory performs collaborative research with Euranova R&D (http://euranova.eu/). The list of subjects proposed for this year by Euranova can be found here
These subject include topics on distributed graph processing, processing big data using Map/Reduce, cloud computing, and social networks.
Due to the increasing availability of larger and larger cheap RAM memories, the working set of modern database management systems becomes more and more main memory resident. This implies that, in contrast to traditional database management systems, slow disk accesses are rare, and that hence, the in-memory processing speed of databases becomes an important factor. As recently observed by a number of researchers, (e.g., Neumann and Leis), one very attractive approach for fast query processing in this context is the just-in-time compilation of incoming queries into machine code. This compilation avoids the overhead of the traditional interpretation of query plans, and can aid in minimzing memory traffic for boosting performance.
A number of recent research prototypes exist that compile SQL queries into machine code in this sense: HyPer A Hybrid OLTP&OLAP High Performance DBMS (http://hyper-db.de/) and Legobase (https://github.com/epfldata/NewLegoBase and http://data.epfl.ch/legobase).
The objective of this master thesis is to apply the same methodology to engineer a compiler that translates (fragments of) SPARQL (the standard query language for querying RDF data on the semantic web) into machine code. The overall methodology should follow the methodology used by HyPer and Legobase:
Getting aquaintend with these technologies is part of the master thesis objective.
Validation of the approach The thesis should propose a benchmark collection of SPARQL queries that can be used to test the obtained SPARQL-to-machine-code compiler and compare its perforance against a reference, interpreter-based SPARQL compiler.
Deliverables of the master thesis project:
Interested? Contact : Stijn Vansummeren
Status: available
Despite the availability of numerous standardized formats for semi-structured and semantic web data such as XML, RDF, and JSON, a very large percentage of data and open data published on the web, remains tabular in nature. (Jeni Tennison, one of the two co-chairs of the W3C CSV on the Web working group claims that ``over 90% of the data published on data.gov.uk is tabular data.) Tabular data is most commonly published in the form of comma separated values (CSV) files because such files are open and therefore processable by numerous tools, and tailored for all sizes of files ranging from a number of KBs to several TBs. Despite these advantages, working with CSV files is often cumbersome because they are typically not accompanied by a schema that describes the file's structure (i.e., ``the second column is of integer datatype, ``columns are delimited by tabs'', etc) and captures its intended meaning. Such a description is nevertheless vital for any user trying to interpret the file and execute queries or make changes to it.
In other data models, the presence of a schema is also important for query optimization (required for scalable query execution if the file is large), as well as other static analysis tasks. Finally, schemas are a prerequisite for unlocking huge amounts of tabular data to the Semantic Web.
In recognition of this problem, the CSV on the Web Working Group of the World Wide Web Consortium argues for the introduction of a schema language for tabular data to ensure higher interoperability when working with datasets using the CSV or similar formats.
The objective of this master thesis is to implement a recent proposal for such a schema language named SCULPT (http://arxiv.org/abs/1411.2351). Concretely, this entails:
Deliverables of this master thesis project:
Interested? Contact: Stijn Vansummeren
Status: available
Automatically extracting structured information from text is a task that has been pursued for decades. As a discipline, /Information Extraction/ (IE) had its start with the DARPA Message Understanding Conference in 1987. While early work in the area focused largely on military applications, recent changes have made information extraction increasingly important to an increasingly broad audience. Trends such as the rise of social media have produced huge amounts of text data, while analytics platforms like Hadoop have at the same time made the analysis of this data more accessible to a broad range of users. Since most analytics over text involves information extraction as a first step, IE is a very important part of data analysis in the enterprise today.
In 2005, researchers at the IBM Almaden Research Center developped a new system specifically geared for practical information extraction in the enterprise. This effort lead to SystemT, a rule-based IE system with an SQL-like declarative language named AQL (Annotation Query Language). The declarative nature of AQL enables new kinds of tools for extractor development, and draws upon known techniques form query processing in relational database management systems to offer a cost-based optimizer that ensures high-througput performance. Recent research into the foundations of AQL (http://researcher.watson.ibm.com/researcher/files/us-fagin/jacm15.pdf) has shown that, as an alternative, it is also possible to build a runtime system for AQL based on special kinds of finite state automata. A potential benefit of this alternate runtime system is that text files need only be processed once (instead of multiple times in the cost-based optimizer backend) and may hence provide greater throughput. On the other hand, the alternate system can sometimes have larger memory requirements than the cost-based optimizer backend.
The objective of this master thesis is to design and engineer a runtime system and compiler for (a fragment) of AQL based on finite state automata. Ideally, to obtain the best performance, these automata should be compiled into machine-code when executed. For this compilation, the following technologies should be used:
Getting aquaintend with these technologies is part of the master thesis objective.
Validation of the approach The thesis should propose a benchmark collection of AQL queries and associated input text files that can be used to test the obtained automaton-based AQL compiler and compare its performance against the reference, cost-based optimizer of SystemT.
Deliverables of the master thesis project:
References about SystemT:
References about finite state automata evaluation:
Interested? Contact : Stijn Vansummeren
Status: available
Recent research in database management systems at ULB has shown how to theoretically construct succinct (compressed) representations for relational databases and semantic web databases. The advantage of these succinct representations is that they allow querying directly on the succinct representation, without needing to consult the underlying database.
The goal of this thesis is to study scalable algorithms for constructing the actual succinct representations. Some in-memory algorithms are already known, but given the large size of typical database, distributed and out-of-core alternatives need to be found.
Deliverables:
Interested? Contact : Stijn Vansummeren
Status: available
Datalog is a fundamental query language in datamanagement based on logic programming. It essentially extends select-from-where SQL queries with recursion. There is a recent trend in data management research to use datalog to specify distributed applications, most notably on the web, as well as do inference on the semantic web. The goal of this thesis is to engineer a basic distributed datalog system, i.e., a system that is capable of compiling & running distributed datalog queries. The system should be implemented in the Scala programming language. Learning Scala is part of the master thesis project.
The system should incorporate recently proposed worst-case join algorithms (i.e., the leapfrog trie join) and employ known local datalog optimizations (such as magic sets and QSQ.)
Validation of the approach The thesis should propose a benchmark collection of datalog queries and associated data workloads that be used to test the obtained system, and measure key performance characteristics (elasticity of the system; memory frootprint; overall running time, …)
Deliverables:
Interested? Contact : Stijn Vansummeren
Status: available
Stijn Vansummeren (WIT), Frédéric Robert (BEAMS)
This master thesis project concerns the analysis, design, and implementation of a software system that can assist in the revision of teaching curricula (also known as teaching programs).
The primary targetted functionalities of the software system are as follows:
A proof-of-concept implementation of a revision tool that supports the first two requirements above is currently being developped in the context of a PROJH402 project. The MFE student that selects this topic is expected to:
Interested? Contact : Stijn Vansummeren (stijn.vansummeren@ulb.ac.be), Frédéric Robert frrobert@ulb.ac.be
Toon Calders (WIT)
For this project a large data collection consisting of historical birth, death, and marriage certificates of the province of North-Brabant in the Netherlands is available. This collection contains certificates for about 3 million people, from 1580 until 1955. This collection of paper documents has been indexed by volunteers. For many of the certificates (unfortunately the index is not complete yet), the names of the people involved in it, and their role have been recorded in a database. Consider for instance the following example of an index entry for a death certificate:
| Death certificate | |
|---|---|
| Deceased | Johanna Louise Fredrika Frans |
| Relation of the deceased | Gerard Cornelius Reincke de Sitter |
| Father of the deceased | Carl Ludwig Frans |
| Mother of the deceased | Alida Philippina Zehender |
| Type of deed | death certificate |
| Number of deed | 5 |
| Place | Beers |
| Date of decease | 26-02-1825 |
| Period | 1825 |
| Contains | Overlijdensregister 1825 |
| Number of inventory | 50 |
| Record number | 456 |
There are, however, several problems with the data recorded by the volunteers:
The goal of this graduation project is to automatically detect name variations for location and person names, using statistical and data mining methods. Because of the large size of the database it is very likely that most name variations occur frequently. In a pilot study, it was shown that name variations could be detected by finding pairs of full names sharing most surnames, but not all. The differences often were name variations. Your task will be to extend this approach to also include locations, and exploit additional background knowledge such as: for most birth certificates there is a matching death certificate, no one has more than one birth and death certificate, etc. This project has a large research component, so your creative input will be required as well. For this project it is absolutely not necessary to speak or understand Dutch.
Interested? Contact Toon Calders
Toon Calders (WIT) and Olivier Debeir (LISA)
The goal of this project is to study the applicability of current state-of-the-art text recognition tools in the following practical application. Consider the following two exemplary documents:
https://dl.dropbox.com/u/5119252/MFE/069-50-3165-1813-00009.jpg
https://dl.dropbox.com/u/5119252/MFE/069-50-3165-1815-00003.jpg
These two documents are scans of birth certificates (actually both are 2 birth certificates) from the Dutch city Grave. We have a huge collection of such paper documents; about 3 million, of which several tens of thousands have been scanned. Furthermore, we have an index on these documents, created by volunteers. This index contains, for the birth certificate, the name of the child, the name of the father and mother, and the witnesses. As you can see in the documents, however, much more information is available. Your task is to answer the following question: is it realistic, given the current state-of-the-art to do automatic recognition of hand-written texts such as these certificates? Most of the documents are very structured, with limited number of possible values (age of a person, profession), and there is a huge amount of training data; the names of all people have been indexed, usually the handwriting is consistent throughout a whole book with certificates. This graduation project includes a thorough literature study and experimentation with (original combinations of) state-of-the-art image recognition techniques adapted to our specific case. The project will be carried out in collaboration with the research labs WIT and LISA.
Interested? Contact Toon Calders
Toon Calders (WIT)
According to a recent report of Price Waterhouse Cooper, the most common source of security incidents are current employees, followed at a distance by former employees and only after that truly external threats such as hactivists. [http://www.pwc.com/gx/en/consulting-services/information-security-survey/giss.jhtml?region=&industry=] This observation leads to the conclusion that in an intelligent security event management system, should also concentrate on internal threats to security. The goal of the thesis is to analyze the possibility of using process mining to help in the detection of silent attacks. We will concentrate on company-specific data. From this data typical behavior will be detected and modeled as a process or workflow. We consider three aspects of a workflow: the actor(s), the resources, and the activities. By modeling the normal behavior in the system we are able to detect deviating cases. Based on historical data, the goal is to build models of typical behavior, including the use of resources such as patient records. Such a system would be able to detect for instance if a certain patient record is consulted much more often than usual, or by more people, or outside of the normal workflow (e.g., only reading information, but not writing). Such a pattern could indicate unjustified access to for instance the patient record of a famous patient. For modeling the workflows, we propose the use of process mining (Van der Aalst, 2011). Process mining is a state-of-the-art technology concerned with the automatic extraction of process models from event logs. Consider, e.g., a hospital registering all activities that are carried out for the treatment of patients, ranging from the admission, various measurements being taken from the patient, medicine administered, surgical procedures, to the resignation of the patient. Process mining could be used to extrapolate from these examples, a common model of how the hospital deals with a patient. There are several applications of process mining; first it can be used to improve the processes by standardizing them; many companies and organizations may only have informal procedures. By process mining the process logs are used to extract a general model of the actual business processes. Such a model can guide the automation process. In this thesis the goal is to analyze how process mining could be used for anomaly detection; how can the discovered models be used to detect abnormal behavior in a company network? Much like in credit card fraud detection, the approach is to first model normal behavior, in this case using process mining, in order to detect diverging behavior that could indicate security breaches in the network.
Van der Aalst, W. M. (2011). Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer.
Interested? Contact Toon Calders
Toon Calders (WIT)
Data mining is the research discipline that studies the extraction of information from large amounts of data. One of the typical data mining tasks is pattern mining where we try to find regularities that occur frequently in a dataset. The prototypical example is that of a supermarket storing for every customer visiting the supermarket, the transaction; that is, the set of items that were bought by that customer. The frequent itemset mining problem now is to detect which combinations of products were more often sold together than a given threshold. One of the major problems of pattern mining algorithms, however, is the enormous amount of redundant patterns they generate; for instance, very popular items, such as toilet paper, tend to appear in many frequent combinations purely due to chance. In order to deal with this problem, techniques based upon compression and minimum description length were proposed to reduce the number of patterns. The rationale behind the minimal description length principle is that a set of patterns that describes well what is happening in the dataset should allow for a good compression. For a collection of patterns, the quality is measured as the description length of the patterns plus the size of the data compressed with these patterns. For instance, if the pattern {bread, milk, butter} has a high frequency, we could opt to replace every occurrence of this pattern by a special code, effectively reducing the encoding length of the data. Surprisingly, however, the MDL principle was until now only used to rule out redundant patterns, and it has not been researched yet how well the discovered patterns actually do compress the data as compared to compression algorithms such as Lempel–Ziv–Welch. Hence, in this highly research oriented graduation project, two research questions are central: (1) How good do non-redundant pattern sets based on MDL allow compressing data, and (2) Can we extract useful patterns from existing compression algorithms?
Interested? Contact Toon Calders
Toon Calders (WIT)
Pattern mining techniques are more and more often used in computer vision to obtain features that are more discriminative than those extracted using computer vision algorithms. This is true for example in content-based images/videos retrieval, indexing, classification, tracking, etc. However, the main drawback of using traditional pattern mining techniques is their inefficiency when dealing with huge set of data (for example provided by Google image or Youtube for videos) or when trying to tackle real-time analysis problems. The data mining community has been working on the “Big Data” problem for many years coming up with promising solutions such as stream mining. The aim of this project is to explore the possibility of using pattern mining in data streams for the (real-time) analysis of videos and, in particular, for object tracking.
For more extensive information regarding the context and problem setting, see the following paper:
Toon Calders, Elisa Fromont, Baptiste Jeudy and Hoang Thanh Lam.
Analysis of Videos using Tile Mining.
In: ECML/PKDD Workshop on Real-World Challenges for Data Stream Mining, Prague, 2013
Interested? Contact Toon Calders
Stijn Vansummeren (WIT), Frédéric Robert (BEAMS)
This MFE concers the analysis, design, and implementation of a software system that can assist in the revision of teaching curricula (also known as teaching programs).
The primary targetted functionalities of the software system are as follows:
A proof-of-concept implementation of a revision tool that supports the first two requirements above is currently being developed in the context of a PROJH402 project. The MFE student that selects this topic is expected to:
Contact : Stijn Vansummeren stijn.vansummeren@ulb.ac.be, Frédéric Robert frrobert@ulb.ac.be
RDF is the W3C proposed framework for representing information in the Web. Basically, information in RDF is represented as a set of triples of the form (subject,predicate,object). RDF syntax is based on directed labeled graphs, where URIs are used as node labels and edge labels. The Linked Open Data (LOD) initiative is aimed at extending the Web by means of publishing various open datasets as RDF, setting RDF links between data items from different data sources. Many companies and government agencies are moving towards publishing data following the LOD initiative. In order to do this, the original data must be transformed into Linked Open Data. Although most of these data are alphanumerical, most of the time they contained a spatial or spatio-temporal component, that must also be transformed. This can be exploited by application providers, that can build attractive and useful applications, in particular, for devices like mobile phones, tablets, etc.
The goals of this thesis are: (1) study the existing proposals for mapping spatio-temporal data into LOD; (2) apply this mapping to a real-world case study (as was the case for the Open Semantic Cloud for Brussels project; (3) Based on the produced mapping, and using existing applications like the Linked Geo Data project, build applications that make use of LOD for example, to find out which cultural events are taking place at a given time at a given location.
SPARQL is the W3C standard language to query RDF data over the semantic web. Although syntactically similar to SQL, SPARQL is based on graph matching. In addition, SPARQL is aimed, basically, to query alphanumerical data. Therefore, a proposal to extend SPARQL to support spatial data, called GeoSPARQL, has been presented to the Open Geospatial Consortium.
In this thesis we propose to (1) perform an analysis of the current proposal for GeoSPARQL; (2) a study of current implementations of SPARQL that support spatial data; (3) implement simple extensions for SPARQL to support spatial data, and use these language in real-world use cases.
