Intuitionistic fuzzy XML query matching and rewriting

With the emergence of XML as a standard for data representation, particularly on the web, the need for intelligent query languages that can operate on XML documents with structural heterogeneity has recently gained a lot of popularity. Traditional Information Retrieval and Database approaches have limitations when dealing with such scenarios. Therefore, fuzzy (flexible) approaches have become the predominant. In this thesis, we propose a new approach for approximate XML query matching and rewriting which aims at achieving soft matching of XML queries with XML data sources following different schemas. Unlike traditional querying approaches, which require exact matching, the proposed approach makes use of Intuitionistic Fuzzy Trees to achieve approximate (soft) query matching. Through this new approach, not only the exact answer of a query, but also approximate answers are retrieved. Furthermore, partial results can be obtained from multiple data sources and merged together to produce a single answer to a query. The proposed approach introduced a new tree similarity measure that considers the minimum and maximum degrees of similarity/inclusion of trees that are based on arc matching. New techniques for soft node and arc matching were presented for matching queries against data sources with highly varied structures. A prototype was developed to test the proposed ideas and it proved the ability to achieve approximate matching for pattern queries with a number of XML schemas and rewrite the original query so that it obtain results from the underlying data sources. This has been achieved through several novel algorithms which were tested and proved efficiency and low CPU/Memory cost even for big number of data sources

A survey on tree matching and XML retrieval

International audienceWith the increasing number of available XML documents, numerous approaches for retrieval have been proposed in the literature. They usually use the tree representation of documents and queries to process them, whether in an implicit or explicit way. Although retrieving XML documents can be considered as a tree matching problem between the query tree and the document trees, only a few approaches take advantage of the algorithms and methods proposed by the graph theory. In this paper, we aim at studying the theoretical approaches proposed in the literature for tree matching and at seeing how these approaches have been adapted to XML querying and retrieval, from both an exact and an approximate matching perspective. This study will allow us to highlight theoretical aspects of graph theory that have not been yet explored in XML retrieval

ZASTOSOWANIE ALGORYTMU WYSZUKIWANIA WIELU WZORCÓW OPARTEGO O TECHNIKĘ Q-GRAMÓW DO WYSZUKIWANIA PRZYBLIŻONEGO

We consider the application of multiple pattern matching (Multi AOSO on q-Grams) algorithm for approximate pattern matching. We propose the on-line approach which translates the problem from approximate pattern matching into a multiple pattern one (called partitioning into exact search). Presented solution allows relatively fast search multiple patterns in text with given k-differences(or mismatches). This paper presents comparison of solution based on MAG algorithm, and [4]. Experiments on DNA, English, Proteins and XML texts with up to k errors show that the new proposed algorithm achieves relatively good results in practical use.Rozważamy zastosowanie algorytmu wyszukiwania wielu wzorców (Multi AOSO on q-Grams) do wyszukiwania przybliżonego. Proponujemy rozwiązanie on-line, upraszczające problem wyszukiwania przybliżonego do wyszukiwania wielu wzorców. Zaprezentowane rozwiązanie umożliwia relatywnie szybko wyszukiwać wiele wzorców dla odległości Levenshteina (lub Hamminga) z ograniczeniem do k. W artykule porównane jest rozwiązanie oparte na algorytmie MAG oraz [4]. Badania eksperymentalne przeprowadzone na zbiorach DNA, English, Proteins and XML z różnymi wartościami k wykazały, że zaproponowany algorytm osiąga relatywnie dobre wyniki w praktycznym zastosowaniu

Term-Specific Eigenvector-Centrality in Multi-Relation Networks

Fuzzy matching and ranking are two information retrieval techniques widely used in web search. Their application to structured data, however, remains an open problem. This article investigates how eigenvector-centrality can be used for approximate matching in multi-relation graphs, that is, graphs where connections of many different types may exist. Based on an extension of the PageRank matrix, eigenvectors representing the distribution of a term after propagating term weights between related data items are computed. The result is an index which takes the document structure into account and can be used with standard document retrieval techniques. As the scheme takes the shape of an index transformation, all necessary calculations are performed during index tim

Investigation into Indexing XML Data Techniques

The rapid development of XML technology improves the WWW, since the XML data has many advantages and has become a common technology for transferring data cross the internet. Therefore, the objective of this research is to investigate and study the XML indexing techniques in terms of their structures. The main goal of this investigation is to identify the main limitations of these techniques and any other open issues. Furthermore, this research considers most common XML indexing techniques and performs a comparison between them. Subsequently, this work makes an argument to find out these limitations. To conclude, the main problem of all the XML indexing techniques is the trade-off between the size and the efficiency of the indexes. So, all the indexes become large in order to perform well, and none of them is suitable for all users’ requirements. However, each one of these techniques has some advantages in somehow

ApproXFILTER - an approximative XML filter

Publish/subscribe systems filter published documents and inform their subscribers about documents matching their interests. Recent systems have focussed on documents or messages sent in XML format. Subscribers have to be familiar with the underlying XML format to create meaningful subscriptions. A service might support several providers with slightly differing formats, e.g., several publishers of books. This makes the definition of a successful subscription almost impossible. We propose the use of an approximative language for subscriptions.We introduce the design our ApproXFILTER algorithm for approximative filtering in a pub/sub system. We present the results of our analysis of a prototypical implementation

Composition of Biochemical Networks using Domain Knowledge

Graph composition has applications in a variety of practical applications. In drug development, for instance, in order to understand possible drug interactions, one has to merge known networks and examine topological variants arising from such composition. Similarly, the design of sensor nets may use existing network infrastructures, and the superposition of one network on another can help with network design and optimisation. The problem of network composition has not received much attention in algorithm and database research. Here, we work with biological networks encoded in Systems Biology Markup Language (SBML), based on XML syntax. We focus on XML merging and examine the algorithmic and performance challenges we encountered in our work and the possible solutions to the graph merge problem. We show that our XML graph merge solution performs well in practice and improves on the existing toolsets. This leads us into future work directions and the plan of research which will aim to implement graph merging primitives using domain knowledge to perform composition and decomposition on specific graphs in the biological domain

Query management in a sensor environment

Traditional sensor network deployments consisted of fixed infrastructures and were relatively small in size. More and more, we see the deployment of ad-hoc sensor networks with heterogeneous devices on a larger scale, posing new challenges for device management and query processing. In this paper, we present our design and prototype implementation of XSense, an architecture supporting metadata and query services for an underlying large scale dynamic P2P sensor network. We cluster sensor devices into manageable groupings to optimise the query process and automatically locate appropriate clusters based on keyword abstraction from queries. We present experimental analysis to show the benefits of our approach and demonstrate improved query performance and scalability