Zohra Bellahsene

Schema Matching and Mapping

Schema Matching and Mapping provides an overview of the ways in which the schema and ontology matching and mapping tools have addressed information systems requirements. Topics include effective methods for matching data, mapping transformation verification, mapping-driven schema evolution and merging.

Database and XML Technologies

XML Twig Queries.- Ordered Backward XPath Axis Processing against XML Streams.- BPI-TWIG: XML Twig Query Evaluation.- On the Efficiency of a Prefix Path Holistic Algorithm.- Query Execution.- KSRQuerying: XML Keyword with Recursive Querying.- The XML-? XPath Processor: Benchmarking and Results.- XPath+: A Tool for Linked XML Documents Navigation.- XML Document Parsing and Compression.- A Data Parallel Algorithm for XML DOM Parsing.- Optimizing XML Compression.- XML Lossy Text Compression: A Preliminary Study.- XQuery.- XQuery Full Text Implementation in BaseX.- Recommending XMLTable Views for XQuery Workloads.- An Encoding of XQuery in Prolog.- Universal XForms for Dynamic XQuery Generation.- XML Transaction Management and Schema Design.- From Entity Relationship to XML Schema: A Graph-Theoretic Approach.- Atomicity for XML Databases.

PORSCHE: Performance ORiented SCHEma mediation

Semantic matching of schemas in heterogeneous data sharing systems is time consuming and error prone. Existing mapping tools employ semi-automatic techniques for mapping two schemas at a time. In a large-scale scenario, where data sharing involves a large number of data sources, such techniques are not suitable. We present a new robust automatic method which discovers semantic schema matches in a large set of XML schemas, incrementally creates an integrated schema encompassing all schema trees, and defines mappings from the contributing schemas to the integrated schema. Our method, PORSCHE (Performance ORiented SCHEma mediation), utilises a holistic approach which first clusters the nodes based on linguistic label similarity. Then it applies a tree mining technique using node ranks calculated during depth-first traversal. This minimises the target node search space and improves performance, which makes the technique suitable for large-scale data sharing. The PORSCHE framework is hybrid in nature and flexible enough to incorporate more matching techniques or algorithms. We report on experiments with up to 80 schemas containing 83,770 nodes, with our prototype implementation taking 587s on average to match and merge them, resulting in an integrated schema and returning mappings from all input schemas to the integrated schema. The quality of matching in PORSCHE is shown using precision, recall and F-measure on randomly selected pairs of schemas from the same domain. We also discuss the integrity of the mediated schema in the light of completeness and minimality measures.

YAM++: a multi-strategy based approach for ontology matching task

In this paper, we present the capability of our ontology matching tool YAM++. We show that YAM++ is able to discover mappings between entities of given two ontologies by using machine learning approach. Besides, we also demonstrate that if the training data are not available, YAM++ can discover mappings by using information retrieval techniques. Finally, we show that YAM++ is able to deal with multi-lingual ontologies matching problem.

Schema evolution in data warehouses

Abstract. In this paper, we address the issues related to the evolution and maintenance of data warehousing systems, when underlying data sources change their schema capabilities. These changes can invalidate views at the data warehousing system. We present an approach for dynamically adapting views according to schema changes arising on source relations. This type of maintenance concerns both the schema and the data of the data warehouse. The main issue is to avoid the view recomputation from scratch especially when views are defined from multiple sources. The data of the data warehouse is used primarily in organizational decision-making and may be strategic. Therefore, the schema of the data warehouse can evolve for modeling new requirements resulting from analysis or data-mining processing. Our approach provides means to support schema evolution of the data warehouse independently of the data sources.

A survey of view selection methods

Materialized view selection is a critical problem in many applications such as query processing, data warehousing, distributed and semantic web databases, etc. We refer to the problem of selecting an appropriate set of materialized views as the view selection problem. Many different view selection methods have been proposed in the literature to address this issue. The present paper provides a survey of view selection methods. It defines a framework for highlighting the view selection problem by identifying the main dimensions that are the basis in the classification of view selection methods. Based on this classification, this study reviews most of the view selection methods by identifying respective potentials and limits.

A Flexible Approach for Planning Schema Matching Algorithms

Most of the schema matching tools are assembled from multiple match algorithms, each employing a particular technique to improve matching accuracy and making matching systems extensible and customizable to a particular domain. The solutions provided by current schema matching tools consist in aggregating the results obtained by several match algorithms to improve the quality of the discovered matches. However, aggregation entails several drawbacks. Recently, it has been pointed out that the main issue is how to select the most suitable match algorithms to execute for a given domain and how to adjust the multiple knobs (e.g. threshold, performance, quality, etc.). In this article, we present a novel method for selecting the most appropriate schema matching algorithms. The matching engine makes use of a decision tree to combine the most appropriate match algorithms. As a first consequence of using the decision tree, the performance of the system is improved since the complexity is bounded by the height of the decision tree. Thus, only a subset of these match algorithms is used during the matching process. The second advantage is the improvement of the quality of matches. Indeed, for a given domain, only the most suitable match algorithms are used. The experiments show the effectiveness of our approach w.r.t. other matching tools.

On Evaluating Schema Matching and Mapping

The increasing demand of matching and mapping tasks in modern integration scenarios has led to a plethora of tools for facilitating these tasks. While the plethora made these tools available to a broader audience, it led to some form of confusion regarding the exact nature, goals, core functionalities, expected features, and basic capabilities of these tools. Above all, it made performance measurements of these systems and their distinction a difficult task. The need for design and development of comparison standards that will allow the evaluation of these tools is becoming apparent. These standards are particularly important to mapping and matching system users, since they allow them to evaluate the relative merits of the systems and take the right business decisions. They are also important to mapping system developers, since they offer a way of comparing the system against competitors, and motivating improvements and further development. Finally, they are important to researchers as they serve as illustrations of the existing system limitations, triggering further research in the area. In this work, we provide a generic overview of the existing efforts on benchmarking schema matching and mapping tasks. We offer a comprehensive description of the problem, list the basic comparison criteria and techniques, and provide a description of the main functionalities and characteristics of existing systems.

Selection of materialized views: a cost-based approach

Recently, multi-query optimization techniques have been considered as beneficial in view selection setting. The main interest of such techniques relies in detecting common sub expressions between the different queries of workload. This feature can be exploited for sharing updates and space storage. However, due to the reuse a query change may entail an important reorganization of the multi query graph. In this paper, we present an approach that is based on multi-query optimization for view selection and that attempts to reduce the drawbacks resulting from these techniques. Finally, we present a performance study using workloads consisting of queries over the schema of the TPC-H benchmark. This study shows that our view selection provides significant benefits over the other approaches.

Opening the Black Box of Ontology Matching

Due to the high heterogeneity of ontologies, a combination of many methods is necessary in order to discover correctly the semantic correspondences between their elements. An ontology matching tool can be seen as a collection of several matching components, each implementing a specific method dealing with a specific heterogeneity type (terminological, structural or semantic). In addition, a mapping selection module is introduced to filter out the most likely mapping candidates. This paper proposes an empirical study of the interaction between these components working together inside an ontology matching system. By the help of datasets from the Ontology Alignment Evaluation Initiative, we have carried out several experimental studies. In the first place, we have been interested in the impact of the mapping selection module on the performance of terminological and structural matchers revealing the advantage of using global methods vs. local ones. Further, we have carried an extensive study on the flaw of the performance of a structural matcher in the presence of noisy input coming from a terminological method. Finally, we have analyzed the behavior of a structural and a semantic component with respect to inputs taken from different terminological matchers.