Alsayed Algergawy

Improving XML schema matching performance using Prüfer sequences

Schema matching is a critical step for discovering semantic correspondences among elements in many data-shared applications. Most of existing schema matching algorithms produce scores between schema elements resulting in discovering only simple matches. Such results partially solve the problem. Identifying and discovering complex matches is considered one of the biggest obstacle towards completely solving the schema matching problem. Another obstacle is the scalability of matching algorithms on large number and large-scale schemas. To tackle these challenges, in this paper, we propose a new XML schema matching framework based on the use of Prufer encoding. In particular, we develop and implement the XPruM system, which consists mainly of two parts-schema preparation and schema matching. First, we parse XML schemas and represent them internally as schema trees. Prufer sequences are constructed for each schema tree and employed to construct a sequence representation of schemas. We capture schema tree semantic information in Label Prufer Sequences (LPS) and schema tree structural information in Number Prufer Sequences (NPS). Then, we develop a new structural matching algorithm exploiting both LPS and NPS. To cope with complex matching discovery, we introduce the concept of compatible nodes to identify semantic correspondences across complex elements first, then the matching process is refined to identify correspondences among simple elements inside each pair of compatible nodes. Our experimental results demonstrate the performance benefits of the XPruM system.

XML data clustering: An overview

In the last few years we have observed a proliferation of approaches for clustering XML documents and schemas based on their structure and content. The presence of such a huge amount of approaches is due to the different applications requiring the clustering of XML data. These applications need data in the form of similar contents, tags, paths, structures, and semantics. In this article, we first outline the application contexts in which clustering is useful, then we survey approaches so far proposed relying on the abstract representation of data (instances or schema), on the identified similarity measure, and on the clustering algorithm. In this presentation, we aim to draw a taxonomy in which the current approaches can be classified and compared. We aim at introducing an integrated view that is useful when comparing XML data clustering approaches, when developing a new clustering algorithm, and when implementing an XML clustering component. Finally, the article moves into the description of future trends and research issues that still need to be faced.

XML Schema Element Similarity Measures: A Schema Matching Context

In this paper, we classify, review, and experimentally compare major methods that are exploited in the definition, adoption, and utilization of element similarity measures in the context of XML schema matching. We aim at presenting a unified view which is useful when developing a new element similarity measure, when implementing an XML schema matching component, when using an XML schema matching system, and when comparing XML schema matching systems.

A schema matching-based approach to XML schema clustering

The relationship between XML data clustering and schema matching is bidirectional. On one side, clustering techniques have been adopted to improve matching performance, and on the other side schema matching is the backbone of the clustering technique. This paper presents a new approach for clustering XML schema based on schema matching. In particular, we develop and implement an XML schema matching system, which determines semantic similarities between XML schemas based on the Prüfer sequence representation of schema trees. The proposed computation similarity algorithm makes use of the semantic meaning of XML elements as well as the hierarchical features of XML schemas. The computed similarities are then exploited by an agglomerative clustering algorithm to group similar schemas. Our experimental results show that the proposed approach is fast and accurate in clustering heterogeneous XML schemas.

SeeCOnt: A New Seeding-Based Clustering Approach for Ontology Matching

Ontology matching plays a crucial role to resolve semantic heterogeneities within knowledge-based systems. However, ontologies contain a massive number of concepts, resulting in performance impediments during the ontology matching process. With the increasing number of ontology concepts, there is a growing need to focus more on large-scale matching problems. To this end, in this paper, we come up with a new partitioning-based matching approach, where a new clustering method for partitioning concepts of ontologies is introduced. The proposed method, called SeeCOnt, is a seeding-based clustering technique aiming to reduce the complexity of comparison by only using clusters’ seed. In particular, SeeCOnt first identifies and determines the seeds of clusters based on the highest ranked concepts using a distribution condition, then the remaining concepts are placed into the proper cluster by defining and utilizing a membership function. The SeeCOnt method can improve the memory consuming problem in the large-scale matching problem, as well as it increases the matching quality. The experimental evaluation shows that SeeCOnt, compared with the top ten participant systems in OAEI, demonstrates acceptable results.

Combining Effectiveness and Efficiency for Schema Matching Evaluation

Schema matching plays a central role in many applications that require interoperability among heterogeneous data sources. A good evaluation for different capabilities of schema matching systems has become vital as the complexity of such systems arises. The capabilities of matching systems incorporate different (possibly conflicting) aspects among them match quality and match efficiency. The analysis of efficiency of a schema matching system, if it is done, tends to be done in a way separate from the analysis of effectiveness. In this paper, we present the trade-off between schema matching effectiveness and efficiency as a multi-objective optimization problem. This representation enables us to obtain a combined measure as a compromise between them. We combine both performance aspects in a weighted-average function to determine the cost-effectiveness of a schema matching system. We apply our proposed approach to evaluate two currently existing mainstream schema matching systems namely COMA++ and BTreeMatch. Experimental results showed that, by carefully utilizing both small-scale and large-scale schemas, it is necessary to take the response time of the matching process into account especially in large-scale schemas.

NNMonitor: Performance modeling for database servers

Database Management Systems (DBMSs) are the cores of most information systems. Database administrators (DBAs) face increasingly more challenges due to the systems growing complexity and must be proficient in areas, such as capacity planning, physical database design, DBMS tuning and DBMS management. Furthermore, DBAs need to implement policies for effective workload scheduling, admission control, and resource provisioning. In response to these challenges we focus our attention on the development of online DBMS performance model. We aim to meet service level agreements (SLAs) and maintain peak performance for DBMS. To this end, we propose a neural network-based performance model called NNMonitor that can predict the performance metrics of DBMS online and determines if the DBMS needs to tune or not before entering into a complex tuning process. We make use of neural networks to build our proposed model taking into account the interaction among concurrently executing queries and predict throughput. The experimental evaluation demonstrates that this model is capable of predicting the performance metrics of real database servers with high accuracy.

A Latent Semantic Indexing-Based Approach to Determine Similar Clusters in Large-scale Schema Matching

Schema matching plays a central role in identifying the semantic correspondences across shared-data applications, such as data integration. Due to the increasing size and the widespread use of XML schemas and different kinds of ontologies, it becomes toughly challenging to cope with large-scale schema matching. Clustering-based matching is a great step towards more significant reduction of the search space and thus improved efficiency. However, methods used to identify similar clusters depend on literally matching terms. To improve this situation, in this paper, a new approach is proposed which uses Latent Semantic Indexing that allows retrieving the conceptual meaning between clusters. The experimental evaluations show encourage results towards building efficient large-scale matching approaches.

EFFICIENTLY LOCATING WEB SERVICES USING A SEQUENCE-BASED SCHEMA MATCHING APPROACH

Locating desired Web services has become a challenging research problem due to the vast number of available Web services within an organization and on the Web. This necessitates the need for developing flexible, effective, and efficient Web service discovery frameworks. To this purpose, both the semantic description and the structure information of Web services should be exploited in an efficient manner. This paper presents a flexible and efficient service discovery approach, which is based on the use of the Prüfer encoding method to construct a one-to-one correspondence between Web services and sequence representations. In this paper, we describe and experimentally evaluate our Web service discovery approach.

MAG: A performance evaluation framework for database systems

Abstract A database system includes a set of different hardware and software resources with a large number of configuration parameters that affect and control the performance of database systems. Tuning these parameters within their diverse and complex environments requires a lot of expertise and it is a time-consuming, and often a misdirected process. Furthermore, tuning attempts often lack a methodology that has a holistic view of the database system. Therefore, in this paper, we introduce MAG, a layer-by-layer tuning framework that can be used to monitor, analyze, predict, and control database configuration parameters. In particular, the framework comprises three main components: NNMonitor predicts the system performance based on its given parameters; Analyzer identifies and determines the source of the problem and then directs to deal with the problem; and NNGenerator generates the experimental training sets exploited in training NNMonitor. The proposed approach focuses on the root causes of database performance problems. The approach further strives avoiding the repetitive trial-and-error process that is a characteristic of a lot of performance-tuning efforts. We experimentally demonstrate the effectiveness of the proposed framework through an extensive set of evaluations.