Browsing by Author "Hegazy, Osman"

Now showing 1 - 3 of 3

ICCPN: Interval-based Conditional Colored Petri Net
(IEEE, 2010) MA Helal, Iman; El-Bastawissy, Ali; Hegazy, Osman
Nowadays, rules make part of any software system including real-time applications and games, meanwhile an event can trigger many different rules according to the conditions controlling these rules. Although rules are core part to many kinds of systems, its maintenance and update are not easy without affecting the whole application. Hence, many systems have presented rules as a separate layer from the application; such as: SAMOS, Sentinel, Snoop, SnoopIB and CCPN. CCPN is a model that was used in an Amplified CDBB-500 architecture; which is a system supporting active database within its architecture. In this paper, we propose some extensions on CCPN to be able to present rules as a separate layer from the application, to support time-based events, and to add other important features which were agreed and implemented in other systems such as: Snoop and SnoopIB
A technique for mutual inconsistencies detection and resolution in virtual data integration environment
(IEEE, 2010) El Qutaany, Ali Zidane; Hegazy, Osman; Hamid El Bastawissy, Ali
Data Integration refers to the problem of combining data residing at homogeneous, autonomous, and heterogeneous data sources, and providing users with a unified global schema. Users pose their queries in terms of this unified global schema. Data integration system allows users to perceive the entire collection as a single source, query it transparently, and receive a single and unambiguous answer. This entire collection of data sources may conflict with each other on three levels: schema level, data representation level, or data level. Most of the approaches in this area of research resolve conflicting among different schemas and among different data representations, and ignore the possibility of data-level conflict altogether; where most of the proposed techniques didn't deal with mutual inconsistencies over consistent data sources. Other techniques deal with mutual inconsistencies but with a set of drawbacks. In this paper a technique for detecting and resolving mutual inconsistencies between data sources is introduced where we assume that the schema level inconsistencies are resolved and the representation of data are unified over all data sources
Towards analternativeData Warehouses Architecture
(Trends in Innovative Computing, 2014) Saddad, Emad; El-Bastawissy, Ali; Hegazy, Osman; Hazman, Maryam
Data warehouses (DWs)are centralized data repositories that integrate data from various transactional, legacy, or external systems, applications, and sources. DW provides an environment separate from the operational systems and is completely designed for decision-support, analytical-reporting, ad-hoc queries, and data mining. Recently, the structure and the volume of data stored on computer systems are growing at an accelerated rate.In current DWs architectures based on n-ary-Relational DBMSs, DWs are increasing their data volume; high disk space consumption; slow query response time, and complex database administration are common problems in these environments. Furthermore, there are a number of factors making developing and maintaininga data warehouse system a painful process such as: setting up a data warehouse can takea long time, over-provisioningcan lead to high costs, organizations may lack the expertise needed to set up and maintain a data warehouse, and system crashes and downtime or system overload can have numerous consequences for an organization. Also, DWs depend on static number of external data sources that may be incomplete, do not use the same definitions, and not always available.The lack of a proper data model and an adequate architecture specifically targeted towards these environments are the root causes of these all problems. So, this paper try to explain why we need an alternative DWs architecture that takes all benefits of existing traditional DWs architecture and solving its problems, dealing with modern environment such as cloud computing, handling in efficient manner the next generation databases (NoSQL) mostly addressing some of the points, and dealing with web applications scalability (such as: big data)