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--- teaching:mfe:is [2015/04/13 14:46]
svsummer [Design and Implementation of a Curriculum Revision Tool]
+++ teaching:mfe:is [2015/09/14 13:40]
svsummer [An implementation of the SCULPT schema language for tabular data on the Web]
@@ Line 19: / Line 19: @@
 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
-{{:teaching:mfe:mt2014_euranova.pdf|here}}
+{{:teaching:mfe:master_thesis_euranova_2015.pdf|here}}
 These subject include topics on distributed graph processing, processing big data using Map/Reduce, cloud computing, and social networks.
   * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]]
+===== Graph Indexing for Fast Subgraph Isomorphism Testing =====
+There is an increasing amount of scientific data, mostly from the bio-medical sciences, that can be represented as collections of graphs (chemical molecules, gene interaction networks, ...). A crucial operation when searching in this data is that of subgraph    isomorphism testing: given a pattern P that one is interested in (also a graph) in and a collection D of graphs (e.g., chemical molecules), find all graphs in G that have P as a   subgraph. Unfortunately, the subgraph isomorphism problem is computationally intractable. In ongoing research, to enable tractable processing of this problem, we aim to reduce the number of candidate graphs in D to which a subgraph isomorphism test needs   to be executed. Specifically, we index the graphs in the collection D by means of decomposing them into graphs for which subgraph   isomorphism *is* tractable. An associated algorithm that filters graphs that certainly cannot match P can then formulated based on ideas from information retrieval.
+In this master thesis project, the student will emperically validate on real-world datasets the extent to which graphs can be decomposed into graphs for which subgraph isomorphism is tractable, and run experiments to validate the effectiveness of the proposed method in terms of filtering power.
+**Interested?** Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]]
+**Status**: available
+=====  Complex Event Processing for Security Analytics=====
+As noted by [[http://home.deib.polimi.it/cugola/Papers/cep_survey.pdf|Cugola and Magara]], "an increasing number of distributed applications requires processing continuously flowing data ("events") from geographically distributed sources at unpredictable rates to obtain timely responses to complex queries. Examples of such applications come from the most disparate fields: from fraud  detection to network intrusion detection systems, from wireless sensor networks to financial tickers, from traffic management to click-stream inspection."
+These requirements have led to the development of a number of systems specifically designed to process information as a flow (or a set of flows) of continues data "events" according to a set of pre-deployed processing rules.  Despite having a common goal, these systems differ in a wide range of aspects, including architecture, data models, rule and pattern languages, and processing mechanisms. In part, this is due to the fact that they were the result of the research efforts of different communities, each one bringing its own view of the problem and its background to the definition of a solution.
+The master thesis is put forward in the context of the SPICES "Scalable Processing and mIning of Complex Events for Security-analytics" research project, funded by Innoviris.
+The objective of this master thesis is to survey the existing systems and compare the strengths and weaknesses when they are applied specifically to the context detecting security breaches (network intrusion, fraud detection, ...), and help, as part of the research project, in the design & implementation of a new system that overcomes these weaknesses.
+**Interested?** Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]]
+**Status**: already taken.
 ===== Compiling SPARQL queries into machine code =====
@@ Line 78: / Line 104: @@
 **Interested?** Contact: [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]]
-**Status**: available
+**Status**: already taken
 ===== Engineering a runtime system and compiler for AQL =====
@@ Line 184: / Line 210: @@
-===== Automatic detection of name variations =====
+===== Semi-Supervised Entity Resolution =====
 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:
+In the big data era large collections of data have become available for analysis. These data, however, often come from different data sources and may contain errors. Consider for instance a company that wants to combine data from marketing and sales in order to see to what extent the targeted marketing campaign has been successful in attracting new customers. A key operation in this analysis is the identification of which records from marketing and sales refer to the same person. In this way it can be determined which targeted potential customers were already clients, and of the contacted non-clients, which ones reacted to the marketing campaign. Furthermore, most likely the records of marketing are far less reliable and formatted differently than those of sales. For instance, the marketing records won't usually contain a client number. The process of linking these sources together and identifying which records refer to the same person is know as entity resolution. Most existing approaches for entity resolution use either a fixed set of pre-determined rules, which may be sub-optimal for the problem at hand, or are based on learning classifiers which requires large amounts of labelled data.
-^ Death certificate ^^
+In this thesis you will study the possibility of entity-resolution in the absence of large collections of labelled data, by exploiting redundancies in the features with which records can be compared in combination with an active learning approach in which volunteers can be asked to label some examples on the fly.
-|Deceased	|Johanna Louise Fredrika Frans |
+\\
-|Relation of the deceased |Gerard Cornelius Reincke de Sitter |
+**Interested?** Contact [[toon.calders@ulb.ac.be|Toon Calders]]
-|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:
-  - Volunteers made mistakes when recording the names
-  - Natural name variations occur; for instance, during the Napoleonic era, Willem preferred to be called Guillaume. After the French left the Netherlands, Willem became Willem again. Other, less spectacular variations: Fredrika versus Frederika.
-  - Another source of variation is the granularity at which locations are reported. Sometimes locations have been reported at suburb or even neighborhood level, whereas in other records only the city is reported.
-  - Also the original data contained errors. For instance, the order of names may have been swapped.
-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@ulb.ac.be|Toon Calders]]
-===== Analyzing state-of-the-art technology for handwritten text recognition in a practical case study =====
-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]] \\
+===== Using Non-Redundant Sequential Pattern Mining for Process Discovery =====
-[[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@ulb.ac.be|Toon Calders]]
-===== Process Mining on Company Data for Detecting Security Breaches =====
 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.
+Process mining is the act of deriving a process model, such as for instance a Petri-net or a BPMN model, based on an event log. An example of such a log could be all events that an insurance company undertakes for pricing a car insurance based on a request from a client. Events could be looking up if the client has been blacklisted, his or her history w.r.t. car accidents, estimating the risk based on car type, age and gender of the requester, making a proposal, soliciting the agreement of the client, in case of disagreement, contacting a manager to approve a special offer, etc. Based on several traces for different clients may allow the automatic reconstruction of a process model. There exist several approaches for process mining, including footprint based algorithms such as Alpha, Alpha+, heuristic algorithms including heuristics miner, genetic algorithms, region based methods, etc. The goal of this thesis is to explore the possibility of using current state-of-the-art data mining algorithms for sequence and episode mining as a basis of a new and improved version of the alpha-algorithm.
-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.
@@ Line 244: / Line 235: @@
 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@ulb.ac.be|Toon Calders]]
-===== Pattern Mining for Object Tracking =====
-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.
-[[http://labh-curien.univ-st-etienne.fr/~fromont/|Analysis of Videos using Tile Mining.]]\\
-In: //ECML/PKDD Workshop on Real-World Challenges for Data Stream Mining//, Prague, 2013
 Interested? Contact [[toon.calders@ulb.ac.be|Toon Calders]]