Database Systems - Present and Future

The database systems have nowadays an increasingly important role in the knowledge-based society, in which computers have penetrated all fields of activity and the Internet tends to develop worldwide. In the current informatics context, the development of the applications with databases is the work of the specialists. Using databases, reach a database from various applications, and also some of related concepts, have become accessible to all categories of IT users. This paper aims to summarize the curricular area regarding the fundamental database systems issues, which are necessary in order to train specialists in economic informatics higher education. The database systems integrate and interfere with several informatics technologies and therefore are more difficult to understand and use. Thus, students should know already a set of minimum, mandatory concepts and their practical implementation: computer systems, programming techniques, programming languages, data structures. The article also presents the actual trends in the evolution of the database systems, in the context of economic informatics.database systems - DBS, database management systems – DBMS, database – DB, programming languages, data models, database design, relational database, object-oriented systems, distributed systems, advanced database systems

A Service Late Binding Enabled Solution for Data Integration from Autonomous and Evolving Databases

Integrating data from autonomous, distributed and heterogeneous data sources to provide a unified vision is a common demand for many businesses. Since the data sources may evolve frequently to satisfy their own independent business needs, solutions which use hard coded queries to integrate participating databases may cause high maintenance costs when evolution occurs. Thus a new solution which can handle database evolution with lower maintenance effort is required. This thesis presents a new solution: Service Late binding Enabled Data Integration (SLEDI) which is set into a framework modeling the essential processes of the data integration activity. It integrates schematic heterogeneous relational databases with decreased maintenance costs for handling database evolution. An algorithm, named Information Provision Unit Describing (IPUD) is designed to describe each database as a set of Information Provision Units (IPUs). The IPUs are represented as Directed Acyclic Graph (DAG) structured data instead of hard coded queries, and further realized as data services. Hence the data integration is achieved through service invocations. Furthermore, a set of processes is defined to handle the database evolution through automatically identifying and modifying the IPUs which are affected by the evolution. An extensive evaluation based on a case study is presented. The result shows that the schematic heterogeneities defined in this thesis can be solved by IPUD except the relation isomorphism discrepancy. Ten out of thirteen types of schematic database evolution can be automatically handled by the evolution handling processes as long as the evolution is represented by the designed data model. The computational costs of the automatic evolution handling show a slow linear growth with the number of participating databases. Other characteristics addressed include SLEDI’s scalability, independence of application domain and databases model. The descriptive comparison with other data integration approaches shows that although the Data as a Service approach may result in lower performance under some circumstances, it supports better flexibility for integrating data from autonomous and evolving data sources

Architecural Principles and Components of Adaptive Process Management Technology

Process-aware information systems (PAIS) must not freeze business processes, but should enable authorized users to deviate from the implemented workflows on-the-fly and to dynamically evolve them over time. While there has been a lot of work on the theoretical foundations of dynamic process changes, there is still a lack of implemented PAIS providing this dynamics. Designing the architecture of such adaptive PAIS, however, constitutes a big challenge due to the high complexity coming with dynamic changes. Besides this, performance, robustness, security and usability of the PAIS must not be affected by the added flexibility. In the AristaFlow project we follow a holistic approach to master this complexity. Based on a conceptual framework for adaptive process management, we have designed a sophisticated architecture for next generation process management technology. This paper discusses major design goals and basic architectural principles, gives insights into selected system components, and shows how change support features can be realized in an integrated and efficient manner

Towards Schema Evolution in Object-aware Process Management Systems

Enterprises want to improve the lifecycle support for their businesses processes by modeling, enacting and monitoring them based on process management systems (PrMS). Since business processes tend to change over time, process evolution support is needed. While process evolution is well understood in traditional activity-centric PrMS, it has been neglected in object-aware PrMS so far. Due to the tight integration of processes and data, in particular, changes of the data and process schemes must be handled in an integrated way; i.e., the evolution of the data schema might affect the process schema and vice versa. This paper presents our overall vision on the controlled evolution of object-aware processes. Further, it discusses fundamental requirements for enabling the evolution of object-aware process schemas in PHILharmonicFlows, a framework targeting at comprehensive support of object-aware processes

Extending Abstract Interpretation to Dependency Analysis of Database Applications

Dependency information (data- and/or control-dependencies) among program variables and program statements is playing crucial roles in a wide range of software-engineering activities, e.g. program slicing, information flow security analysis, debugging, code-optimization, code-reuse, code-understanding. Most existing dependency analyzers focus on mainstream languages and they do not support database applications embedding queries and data-manipulation commands. The first extension to the languages for relational database management systems, proposed by Willmor et al. in 2004, suffers from the lack of precision in the analysis primarily due to its syntax-based computation and flow insensitivity. Since then no significant contribution is found in this research direction. This paper extends the Abstract Interpretation framework for static dependency analysis of database applications, providing a semantics-based computation tunable with respect to precision. More specifically, we instantiate dependency computation by using various relational and non-relational abstract domains, yielding to a detailed comparative analysis with respect to precision and efficiency. Finally, we present a prototype semDDA, a semantics-based Database Dependency Analyzer integrated with various abstract domains, and we present experimental evaluation results to establish the effectiveness of our approach. We show an improvement of the precision on an average of 6% in the interval, 11% in the octagon, 21% in the polyhedra and 7% in the powerset of intervals abstract domains, as compared to their syntax-based counterpart, for the chosen set of Java Server Page (JSP)-based open-source database-driven web applications as part of the GotoCode project