In most new commercial business software applications like Customer Relationship Management, the data is stored in the database layer which is usually a Relational Database Management System (RDBMS) like Oracle, DB2 UDB or SQL Server. To access data from these databases, Structured Query Language (SQL) queries are used that are generated dynamically at run time based on defined business models and business rules. One such business rule is visibility-the capability of the application to restrict data access based on the role and responsibility of the user logged in to the application. This is generally achieved by appending security predicates in the form of sub-queries to the main query based on the roles and responsibility of the user. In some cases, the outer query may be more restrictive while in other cases, the security predicates may be more restrictive. This often results in a dilemma for the cost-based optimizer (CBO) of the backend database whether to drive from the outer query or drive from the security predicate sub-queries. This dilemma is sometimes called the "Tiny-Huge, Huge-Tiny" problem and results in serious performance degradation by way of increased response times on the application User Interface (UI). This paper provides a case study of a new approach to vastly reduce this CBO dilemma by a combination of denormalized columns and re-writing of the security predicates' sub-queries at run-time, thereby levelling the outer and security sub-queries. This approach results in more stable execution plans in the database and much better performance of such SQLs, effectively leading to higher performance and scalability of the application.

This paper describes a prototype of extended XDB. XDB is an open-source and extensible database architecture developed by National Aeronautics and Space Administration (NASA) to provide integration of heterogeneous and distributed information resources for scientific and engineering applications. XDB enables an unlimited number of desktops and distributed information sources to be linked seamlessly and efficiently into an information grid using Data Access and Retrieval Composition (DARC) protocol which provides a contextual search and retrieval capability useful for lightweight web applications. This paper shows the usage of XDB on common data management in the enterprise without burdening users and application developers with unnecessary complexity and formal schemas. Supported by NASA Ames Research Center through NASA Exploration System Mission Directorate (ESMD) Higher Education grant, a project team at Fairfield University extended this concept and developed an extended XDB protocol and a prototype providing text-searches for Wiki. The technical specification of the protocol was posted to Source Forge (sourceforge.net) and a prototype providing text-searches for Wiki was developed. The prototype was created for 16 tags of the MediaWiki dialect. As part of future works, the prototype will be further extended to the complete Wiki markups and other dialects of Wiki.

In this paper, we are interested in the use of domain ontologies as a semantic enrichment for traditional databases.Our first aim is to help the user in his search when his initial query doesn't return any result.So, we propose a solution based on two different approaches allowing the user to express his interrogation in a relatively free way.The first approach detects and resolves naming and schematic conflicts.It is an ontological approach for SQL query expansion generating a set of queries.The second one is a join detection approach to eventually add all missed constraints in each generated query.

In recent years, we have witnessed the emergence of new types of systems that deal with large volumes of streaming data.Examples include financial data analysis on feeds of stock tickers, sensorbased environmental monitoring, network track monitoring and click stream analysis to push customized advertisements or intrusion detection.Traditional database management systems (DBMS), which are very good at managing large volumes of stored data, are not suitable for this, as streaming needs low-latency processing on live data from push-based sources.Data Stream Management Systems (DSMS) are fast emerging to address this new type of data, but faces challenging issues, such as unpredictable data arrival rate.On bursty mode, processing need surpasses available system capacity affecting the Quality of Service (QoS) adversely.The system overloading is even more acute in XML data streams compared to relational streams due to its extra resource requirements for data preparation and result construction.The main focus of this paper is to find out suitable ways to process this high volume of data streams dealing with the spikes in data arrival gracefully, under limited or fixed system resources in the XML stream context.

The Internet presents large amount of useful information which is usually formatted for its users, which makes it hard to extract relevant data from diverse sources. Therefore, there is a significant need of robust, flexible Information Extraction (IE) systems that transform the web pages into program friendly structures such as a relational database will become essential. IE produces structured data ready for post processing. Roadrunner will be used to extract information from template web pages. In this paper, we present novel algorithm for extracting templates from a large number of web documents which are generated from heterogeneous templates. The proposed system focuses on information extraction from heterogeneous web pages. We cluster the web documents based on the common template structures so that the template for each cluster is extracted simultaneously. The resultant clusters will be given as input to the Roadrunner system.

In this research paper we have explained and presented the results of a novel database recovery method. Taking into account that most data servers today are becoming increasingly autonomic and business process oriented, there is a need to develop a new model where it is possible to do a database recovery based on the occurrence of a business process rather than non intuitive data like time, log file name/location or system change number. The advantage of this recovery model is that the user will no longer be required to remember non intuitive data like timestamp up to which the data has to be restored, log file name/location or system change number. Moreover the database server has built in intelligence to identify transaction behaviour, study the pattern among these batches and then storing recovery information specific to these business process to be used later for recovery. Another advantage is that the down time needed for restoring the database is reduced drastically since the trial and error approach followed by conventional data servers in the absence of accurate recovery information is eliminated. We have also simulated the Idea using JAVA API's and Apache Derby and presented the experimental results for reference..

Following the development of fuzzy logic theory by Lotfi Zadeh, its applications were investigated by researchers in different fields. Presenting and working with uncertain data is a complex problem. To solve for such a complex problem, the structure of relationships and operators dependent on such relationships must be repaired. The fuzzy database has integrity limitations including data dependencies. In this paper, first fuzzy multivalued dependency based semantic proximity and its problems are studied. To solve these problems, the semantic proximity's formula is modified, and fuzzy multivalued dependency based on the concept of extension of semantic proximity with \alpha degree is defined in fuzzy relational database which includes Crisp, NULL and fuzzy values, and also inference rules for this dependency are defined, and their completeness is proved. Finally, we will show that fuzzy functional dependency based on this concept is a special case of fuzzy multivalued dependency in fuzzy relational database.

Several applications involve a transient stream of data which has to be modeled and analyzed continuously.Their continuous arrival in multiple, rapid, time-varying and possibly unpredictable and unbounded way make the analysis difficult and opens fundamentally new research problems.Examples of such data intensive applications include stock market, road traffic analysis, whether forecasting systems etc.In this study, we have used a Data Stream Management System tool-Stanford STREAM to model and analyze data from two different application domains-Road Traffic analysis and Habitat Monitoring analysis.Based on the results we discuss advantages and disadvantages of STREAM.

Providing a customized support for the OLAP brings tremendous challenges to the OLAP technology. Standing at the crossroads of the preferences and the data warehouse, two emerging trends are pointed out; namely: (i) the personalization and (ii) the recommendation . Although the panoply of the proposed approaches, the user-centric data warehouse community issues have not been addressed yet. In this paper we draw an overview of several user centric data warehouse proposals. We also discuss the two promising concepts in this issue, namely, the personalization and the recommendation of the data warehouses. We compare the current approaches among each others with respect to some criteria.

Knowledge can be represented by ontology. In an enterprise context, they reflect the relevant knowledge based on enterprise-specific concepts and their relations. In order to develop ontology, there are various methodologies, where each one may have some pitfalls depending on context .In this paper, based on an analysis of existing methodologies, we explore the possibilities by proposing an integrated model for developing ontology, which can be used to build any kind of ontology. Our main intention is to reduce development time and effort. We had proposed the system with respect to shopping mall domain, where dynamically ontologies can be prepared to get the information faster and correct. Further, these ontologies can be used for mapping. We compare our model with the existing developing methodology, and we had tried to remove the possible pitfalls of the existing techniques .