Data Mining-based Fragmentation of XML Data Warehouses

With the multiplication of XML data sources, many XML data warehouse models have been proposed to handle data heterogeneity and complexity in a way relational data warehouses fail to achieve. However, XML-native database systems currently suffer from limited performances, both in terms of manageable data volume and response time. Fragmentation helps address both these issues. Derived horizontal fragmentation is typically used in relational data warehouses and can definitely be adapted to the XML context. However, the number of fragments produced by classical algorithms is difficult to control. In this paper, we propose the use of a k-means-based fragmentation approach that allows to master the number of fragments through its $k$ parameter. We experimentally compare its efficiency to classical derived horizontal fragmentation algorithms adapted to XML data warehouses and show its superiority

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Optimized Indexes for Data Structured Retrieval

The aim of this work is to show the novel index structure based suffix array and ternary search tree with rank and select succinct data structure. Suffix arrays were originally developed to reduce memory consumption compared to a suffix tree and ternary search tree combine the time efficiency of digital tries with the space efficiency of binary search trees. Rank of a symbol at a given position equals the number of times the symbol appears in the corresponding prefix of the sequence. Select is the inverse, retrieving the positions of the symbol occurrences. These operations are widely used in information retrieval and management, being the base of several data structures and algorithms for text collections, graphs, trees, etc. The resulting structure is faster than hashing for many typical search problems, and supports a broader range of useful problems and operations. There for we implement a path index based on those data structures that shown to be highly efficient when dealing with digital collection consist in structured documents. We describe how the index architecture works and we compare the searching algorithms with others, and finally experiments show the outperforms with earlier approaches

Keyword-based search in peer-to-peer networks

Ph.DDOCTOR OF PHILOSOPH

Processing techniques for partial tree-pattern queries on XML data

In recent years, eXtensible Markup Language (XML) has become a de facto standard for exporting and exchanging data on the Web. XML structures data as trees. Querying capabilities are provided through patterns matched against the XML trees. Research on the processing of XML queries has focused mainly on tree-pattern queries. Tree-pattern queries are not appropriate for querying XML data sources whose structure is not fully known to the user, or for querying multiple data sources which structure information differently. Recently, a class of queries, called Partial Tree-Pattern Queries (PTPQs) was identified. A central feature of PTPQs is that the structure can be specified fully, partially, or not at all in a query. For this reason. PTPQs can be used for flexibly querying XML data sources. This thesis deals with processing techniques for PTPQs. In particular, it addresses the satisfiability, containment and minimization problems for PTPQs. In order to cope with structural expression derivation issues and to compare PTPQs, a set of inference rules is suggested and a canonical form for PTPQs that comprises all derived structural expressions is defined. This canonical form is used for determining necessary and sufficient conditions for PTPQ satisfiability. The containment problem is studied both in the absence and in the presence of structural summaries of data called dimension graphs. It is shown that this problem cannot be characterized by homomorphisms between PTPQs, even when PTPQs are put in canonical form. In both cases of the problem, necessary and sufficient conditions for PTPQ containment are provided in terms of homomorphisms between PTPQs and (a possibly exponential number of) tree-pattern queries. This result is used to identify a subclass of PTPQs that strictly contains tree-pattern queries for which the containment problem can be fully characterized through the existence of homomorphisms. To cope with the high complexity of PTPQ containment, heuristic approaches for this problem are designed that trade accuracy for speed. The heuristic approaches equivalently add structural expressions to PTPQs in order to increase the possibility for a homomorphism between two contained PTPQs to exist. An implementation and extensive experimental evaluation of these heuristics shows that they are useful in practice, and that they can be efficiently implemented in a query optimizer. The goal of PTPQ minimization is to produce an equivalent PTPQ which is syntactically smaller in size. This problem is studied in the absence of structural summaries. It is shown that PTPQs cannot be minimized by removing redundant parts as is the case with certain classes of tree-pattern queries. It is also shown that, in general, a PTPQ does not have a unique minimal equivalent PTPQ. Finally, sound, but not complete, heuristic approaches for PTPQ minimization are presented. These approaches gradually trade execution time for accuracy

Ant colony optimization based clustering for data partitioning.

Woo Kwan Ho.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 148-155).Abstracts in English and Chinese.Contents --- p.iiAbstract --- p.ivAcknowledgements --- p.viiList of Figures --- p.viiiList of Tables --- p.xChapter Chapter 1 --- Introduction --- p.1Chapter Chapter 2 --- Literature Reviews --- p.7Chapter 2.1 --- Block Clustering --- p.7Chapter 2.2 --- Clustering XML by structure --- p.10Chapter 2.2.1 --- Definition of XML schematic information --- p.10Chapter 2.2.2 --- Identification of XML schematic information --- p.12Chapter Chapter 3 --- Bi-Tour Ant Colony Optimization for diagonal clustering --- p.15Chapter 3.1 --- Motivation --- p.15Chapter 3.2 --- Framework of Bi-Tour Ant Colony Algorithm --- p.21Chapter 3.3 --- Re-order of the data matrix in BTACO clustering method --- p.27Chapter 3.3.1 --- Review of Ant Colony Optimization --- p.29Chapter 3.3.2 --- Bi-Tour Ant Colony Optimization --- p.36Chapter 3.4 --- Determination of partitioning scheme --- p.44Chapter 3.4.1 --- Weighed Sum of Error (WSE) --- p.48Chapter 3.4.2 --- Materialization of partitioning scheme via hypothetic matrix --- p.50Chapter 3.4.3 --- Search of best-fit hypothetic matrix --- p.52Chapter 3.4.4 --- Dynamic programming approach --- p.53Chapter 3.4.5 --- Heuristic partitioning approach --- p.57Chapter 3.5 --- Experimental Study --- p.62Chapter 3.5.1 --- Data set --- p.63Chapter 3.5.2 --- Study on DP Approach and HP Approach --- p.65Chapter 3.5.3 --- Study on parameter settings --- p.69Chapter 3.5.4 --- Comparison with GA-based & hierarchical clustering methods --- p.81Chapter 3.6 --- Chapter conclusion --- p.90Chapter Chapter 4 --- Application of BTACO-based clustering in XML database system --- p.93Chapter 4.1 --- Introduction --- p.93Chapter 4.2 --- Overview of normalization and vertical partitioning in relational DB design --- p.95Chapter 4.2.1 --- Normalization of relational models in database design --- p.95Chapter 4.2.2 --- Vertical partitioning in database design --- p.98Chapter 4.3 --- Clustering XML documents --- p.100Chapter 4.4 --- Proposed approach using BTACO-based clustering --- p.103Chapter 4.4.1 --- Clustering XML documents by structure --- p.103Chapter 4.4.2 --- Clustering XML documents by user transaction patterns --- p.109Chapter 4.4.3 --- Implementation of Query Manager for our experimental study --- p.114Chapter 4.5 --- Experimental Study --- p.118Chapter 4.5.1 --- Experimental Study on the clustering by structure --- p.118Chapter 4.5.2 --- Experimental Study on the clustering by user access patterns --- p.133Chapter 4.6 --- Chapter conclusion --- p.141Chapter Chapter 5 --- Conclusions --- p.143Chapter 5.1 --- Contributions --- p.144Chapter 5.2 --- Future works --- p.146Bibliography --- p.148Appendix I --- p.156Appendix II --- p.168Index tables for Profile A --- p.168Index tables for Profile B --- p.171Appendix III --- p.17

A Join Index for XML Data Warehouses

XML data warehouses form an interesting basis for decision-support applications that exploit complex data. However, native-XML database management systems (DBMSs) currently bear limited performances and it is necessary to research for ways to optimize them. In this paper, we propose a new join index that is specifically adapted to the multidimensional architecture of XML warehouses. It eliminates join operations while preserving the information contained in the original warehouse. A theoretical study and experimental results demonstrate the efficiency of our join index. They also show that native XML DBMSs can compete with XML-compatible, relational DBMSs when warehousing and analyzing XML data.Comment: 2008 International Conference on Information Resources Management (Conf-IRM 08), Niagra Falls : Canada (2008

Solving the intractable problem: optimal performance for worst case scenarios in XML twig pattern matching

In the history of databases, eXtensible Markup Language (XML) has been thought of as the standard format to store and exchange semi-structured data. With the advent of IoT, XML technologies can play an important role in addressing the issue of processing a massive amount of data generated from heterogeneous devices. As the number and complexity of such datasets increases there is a need for algorithms which are able to index and retrieve XML data efficiently even for complex queries. In this context twig pattern matching , finding all occurrences of a twig pattern query (TPQ), is a core operation in XML query processing. Until now holistic joins have been considered the state-of-the-art TPQ processing algorithms, but they fail to guarantee an optimal evaluation except at the expense of excessive storage costs which limit their scope in large datasets. In this article, we introduce a new approach which significantly outperforms earlier methods in terms of both the size of the intermediate storage and query running time. The approach presented here uses Child Prime Labels (Alsubai & North, 2018) to improve the filtering phase of bottom-up twig matching algorithms and a novel algorithm which avoids the use of stacks, thus improving TPQs processing efficiency. Several experiments were conducted on common benchmarks such as DBLP, XMark and TreeBank datasets to study the performance of the new approach. Multiple analyses on a range of twig pattern queries are presented to demonstrate the statistical significance of the improvements