Specification of Components Based on the WebComposition Component Model

Developing application systems that use the World Wide Web (WWW, Web) as an application platform suffers from the absence of disciplined approaches to develop such Web-applications. Besides, the Web’s implementation model makes it difficult to apply well-known process models to the development and evolution of Web-applications. On the other hand, component-based software development appears as a promising approach that meets essential requirements of developing and evolving highly dynamic Web-applications. With respect to Web-applications, its main objective is to build Web-applications from (standardized) components. Founded on these insights and based on a dedicated component model, we propose an approach to a disciplined specification of components.

Efficient Query Processing with Structural Join Indexing in an Object Relational Data Warehousing Environment

In an Object Relational Data Warehousing (ORDW) environment, the semantics of data and queries can be explicitly captured, represented, and utilized based on is-a and class composition hierarchies, thereby resulting in more efficient OLAP query processing. In this chapter, we show the efficacy in building semantic-rich hybrid data indexes incorporating Structural Join Index Hierarchy (SJIH) on the ORDW views. Given a set of queries, we use a hill-climbing heuristic algorithm to select (near) optimal SJIHs, thereby embedding query semantics into the indexing framework. Finally, by a cost model, we analyze the effectiveness of our approach vis-a-vis the pointer chasing approach.

Data Mining

During the early years of database management the contemporary wisdom was to store only ‘useful data.’ In large part, this philosophy was encouraged because of the then-limited storage capacity offered by the prevailing technology. Then along came the microprocessor revolution, enormously expanding the scope of data storage. Subsequent advancement in information technology and recognition of potential business opportunities thereof, resulted in enormous expansion of data storage. Although the future is unknown and unpredictable, it often provides new business opportunities. Thus, new management strategies emerged which encouraged the massive accumulation of data and thus the advent of data warehousing. These massive data depositories are now providing both challenges and opportunities for strategic decision-making concerned with improving existing businesses and exploring new business opportunities. Data mining is an essential part of the process involved in locating relevant information from data warehouses for use in making such strategic decisions. Naturally, business leaders everywhere are willing to make investments in corporate data warehouses to enhance their access to information. The return on such investment is by no means guaranteed but all business activities include a certain amount of risk.

An Introduction to Information Technology and Business Intelligence

The world of commerce has undergone a transformation since the early 1990s, which has increasingly included the utilization of information technologies by firms across industry sectors in order to achieve greater productivity and profitability. In other words, through use of such technologies as mainframes, PCs, telecommunications, state-of-the-art software applications and the Internet, corporations seek to utilize productive resources in a way that augment the efficiency with which they provide the most appropriate mix of goods and services to their ultimate consumer. This process has provided the backbone to the evolution of the information economy which has included increased investment in information technology (IT), the demand for IT labor and the initiation of such new paradigms as e-commerce.

The Development of Ordered SQL Packages to Support Data Warehousing

Data warehousing is a corporate strategy that needs to integrate information from several sources of separately developed Database Management Systems (DBMSs). A future DBMS of a data warehouse should provide adequate facilities to manage a wide range of information arising from such integration. We propose that the capabilities of database languages should be enhanced to manipulate user-defined data orderings, since business queries in an enterprise usually involve order. We extend the relational model to incorporate partial orderings into data domains and describe the ordered relational model. We have already defined and implemented a minimal extension of SQL, called OSQL, which allows querying over ordered relational databases. One of the important facilities provided by OSQL is that it allows users to capture the underlying semantics of the ordering of the data for a given application. Herein we demonstrate that OSQL aided with a package discipline can be an effective means to manage the inter-related operations and the underlying data domains of a wide range of advanced applications that are vital in data warehousing, such as temporal, incomplete and fuzzy information. We present the details of the generic operations arising from these applications in the form of three OSQL packages called: OSQL_TIME, OSQL_INCOMP and OSQL_FUZZY.

Complementing the Data Warehouse with Information Filtered from the Web

The data warehouse is considered to be the best way to organize transactional data. However, as many researches claim data warehouse should be augmented with external textual information. The objective of this chapter is to examine the requirements for profiling in the data warehouse environment. Profiles created in the data warehouse are then utilized to filter information. The goal of the sketched system is to support users in his situated actions. We explore many issues concerning personalization, such as information overflow, user models, and situatedness. We also analyze the factors that influence the filtering process. Finally, we draw some conclusions that should be considered during extension of the evaluated system.

Using Business Rules within a Design Process of Active Databases

Modeling behavior is an important task of the information system engineering process. This task is especially important when information systems are centered on active databases, which allow the replacement of parts of application programs with active rules. To relieve programmers from using either traditional or ad hoc techniques to design active databases, it is necessary to develop new techniques to model business rules. For that reason, inclusion of rules during analysis and design stages becomes an actual requirement. In this paper, we propose a uniform approach to modeling business rules (active rules, integrity constraints, etc. ). To improve the behavior specification, we extend the state diagrams that are widely used for dynamic modeling. This extension is a transformation of state transitions according to rule semantics. In addition, we outline new functionalities of Computer Aided System Engineering (CASE) to take into consideration the active database specificities. In this way, the designer can be assisted to control, maintain, and reuse a set of rules.

Justifying Data Warehousing Investments

Organizations are increasingly recognizing the importance of information technology. Many large IT projects in the area of data warehousing and data mining have been taken up in the last few years. While many data warehousing and data mining projects have resulted in interesting business benefits, there are also many examples of cost and schedule overruns and dissatisfaction regarding the results from these projects. A recent issue of Information Week (May 24, 1999) reported that organizations are carefully scrutinizing the returns from large data warehousing projects. This makes it increasingly important for information systems professionals to understand the payoff from data warehousing investments. It is also extremely important for information systems professionals to articulate the business benefits of data warehousing and other big ticket information technology projects in terms that senior managers in general and finance executives in particular can relate to. This article outlines an approach to justifying data warehousing investments that is based on the concept of options in finance. This approach to justifying investments is being increasingly recognized as being superior to traditional methods by finance professionals (Business Week, June 7, 1999).

Benchmarking Data Mining Algorithms

Data mining is the process of sifting through the mass of organizational (internal and external) data to identify patterns critical for decision support. Successful implementation of the data mining effort requires a careful assessment of the various tools and algorithms available. The basic premise of this study is that machine-learning algorithms, which are assumption free, should outperform their traditional counterparts when mining business databases. The objective of this study is to test this proposition by investigating the performance of the algorithms for several scenarios. The scenarios are based on simulations designed to reflect the extent to which typical statistical assumptions are violated in the business domain. The results of the computational experiments support the proposition that machine learning algorithms generally outperform their statistical counterparts under certain conditions. These can be used as prescriptive guidelines for the applicability of data mining techniques.

Incremental Data Allocation and Reallocation in Distributed Database Systems

In a distributed database system, an increase in workload typically necessitates the installation of additional database servers followed by the implementation of expensive data reorganization strategies. We present the Partial REALLOCATE and Full REALLOCATE heuristics for efficient data reallocation. Complexity is controlled and cost minimized by allowing only incremental introduction of servers into the distributed database system. Using first simple examples and then, a simulator, our framework for incremental growth and data reallocation in distributed database systems is shown to produce near optimal solutions when compared with exhaustive methods.