Master thesis data mining
The need to discover and generate frequent graph patterns arises in applications that span a wide spectrum, such as mining molecular data and classification of chemical minig pounds, behavior and link analysis in social networks, workflow analysis, and text classification. Several algorithms for generating maximal frequent subgraphs have been proposed and evaluated experimentally, but until recently not much masger been known about the computational complexity of the fundamental related enumeration and decision problems.
In a recent work, we conducted on a comprehensive investigation of the computational complexity of mining maximal frequent subgraphs, taking into account various key mibing of the problem, such as possible restrictions on the classes of mmining and patterns, bounding the threshold, and bounding the number of desired answers.
Within that investigation, we devised a novel algorithmic framework based on our here work on hereditary graph properties. This framework is click the following article different from previous algorithms for mining maximal frequent subgraphs, as it is incremental in nature maater. The project aims to advance our understanding and the practical usability of mining maximal frequent subgraphs.
We are especially interested in usability in the context of feature generation in machine learning; there, frequent patterns capture recurring patterns in structure, and in turn can be used as features. Benny Theeis, Phokion G. The Complexity of Mining Maximal Frequent Subgraphs. The complexity of mining maximal frequent master thesis data mining. Generating all maximal induced subgraphs for hereditary and connected-hereditary graph properties. Managing Inconsistent Databases with Tuple Preferences Matching background: Logic,databases, complexity theory Abstract: Managing data minig has been one of the major challenges in the research and practice mzster database management.
Sources of inconsistency include imprecise processes of data generation such as mistakes in manual form filling and noisy sensing equipment, as well as data integration where different source databases may contain conflicting information.
The framework of database repairs provides a principled approach to managing inconsistencies in databases. There are situations, however, in which it is natural and desired to prefer one repair over another; for example, one data source is regarded to be more reliable than another, or timestamp information implies that a more recent fact should be preferred over an earlier one. Dzta by these considerations, Staworko, Chomicki and Marcinkowski introduced the framework of mqster repairs. The main characteristic of this framework is that it mininv a priority relation between conflicting facts of an inconsistent database to define notions of preferred repairs.
In this project, htesis goal is to establish the complexity of fundamental tasks such as finding the consistent answers to a query i. Mwster Fagin, Benny Kimelfeld, Frederick Reiss, Stijn Vansummeren: Cleaning inconsistencies in information extraction visit web page prioritized repairs. Dichotomies masteer the Complexity of Preferred Repairs. To appear in PODS, Extending Database Technology with Fundamentals of Text Analytics Matching background: Modern technological and social trends, such as mobile computing and social networking, result mininy an enormous amount of publicly available data with a high potential value within.
Contemporary business models, such as cloud computing, open-source software and crowd sourcing, provide the means for analysis without the resources of minihg enterprises.
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But that data have characteristics that challenge traditional database systems. Due to the uncontrolled nature by which data is produced, much of it is free text, often in informal natural language, leading to computing environments with high levels of uncertainty and error.
Traditional database systems are based on models that fundamentally lack the ability to deeply process text and reason about uncertainty thereof; hence, existing solutions are often software bundles that combine databases, scripts fortext extraction, NLP algorithms, and statistical libraries. The goal of this project is to establish foundations and implementations of data management systems that capture the nature of modern text analysis, to facilitate, expedite, and simplify application development. Database principles in information extraction. Query Suggestion in Complex Schemas Matching background: This research aims to facilitate database querying in settings that involve large and complicated schemas e.
In this project we design and implement a system for automatic suggestion of database queries from keywords, natural text, and visual input from an interactive system. Such a system entails three main challenges. First, a nontrivial interactive interface is needed in order to allow users to express complicated schema relationships and understand system proposals.
Query Suggestion in Mastwr Schemas Matching background: The framework of database repairs provides a principled approach to managing inconsistencies in databases. Benny Kimelfeld, Phokion G. In this project we design and implement a system for automatic suggestion of database queries from keywords, natural text, and visual input from an interactive system.
Second, the translation of vague phrases into valid queries e. Finally, all involved algorithms need to run in interactive response time. Benny Kimelfeld, Yehoshua Sagiv: Extracting minimum-weight tree patterns from a schema with neighborhood constraints. Finding a minimal tree pattern under neighborhood constraints. Understanding queries in a search database system. Rewrite rules for search database systems. Keyword proximity search in complex data graphs.
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Examples of such applications come from the most disparate fields: 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.
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.
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: 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: Use of a high-level language to construct the compiler Scala, http: 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.
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Deliverables of the master thesis project: An overview of the state of the art in query-to-machine-code compilation. A description of latent modular staging and how it can be used to construct machine-code compilers. The SPARQL compiler software artifact A benchmark set of SPARQL queries and associated data sets for the experimental validation An experimental validation of the compiler, comparing efficiency of compiled queries against a reference compiler based on query plan interpretation.
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.
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 largeas 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: 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. Inresearchers 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: 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: A a high-level language to construct the compiler Scala, http: 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. An overview of AQL, SystemT, and its cost-based optimizer and evaluation engine.