Data management: issues and solutions for workflow efficiency.
ABSTRACT Information is a valuable asset in an organization and for this good data management practice is necessary to any technology based organization, as data management ought to be the most persistent discipline and activity in an organization. For this, between 2000 and 2006 IEEE conducted the assessment of 175 organizations to understand data management practice followed by them, and most of the organizations scored low on this aspect. It is unfortunate that worldwide, most organizations do not manage data well. The incorrect data at source travels across all systems and applications and wreaks havoc. Organizations should see how long data must be retained, and frame policies accordingly, for data maintaining, its quality, proper storage, cost of data, destroy, ownership, accessing, accuracy, retention requirements, responsibilities, supervision and recording etc. Undoubtedly data set needs to be accurate and consistence across the organization. In last decade, there has been a spiral growth in business as well as in data, resulting in decentralization of data. This has compelled to bring radical changes in management of data. Moreover, presently many organization measure data management on the scale of "Return on investment" and hence data management process is gaining management's understanding and appreciation. Keeping above in mind, this paper has attempted to illuminate perspectives of various data management issues and solutions in general.
- SourceAvailable from: Zachary G. Ives[Show abstract] [Hide abstract]
ABSTRACT: The Internet community has recently been focused on peer-to-peer systems like Napster, Gnutella, and Freenet, The grand vision a decentralized community of machines pooling their resources to benefit everyone is compelling for many reasons: scalability, robustness, lack of need for administration, and even anonymity and resistance to censorship. Esisting peer-to-peer (P2P) systems have focused on specific application domains (e.g. music files) or on providing file-system-like capabilities; these systems ignore the semantics of data, An important question for the database communityis how data management can be applied to P2P, and what we can learn from and contribute to the P2P area. We address these questions, identify a number of potential research ideas in the overlap between data management and P2P systems, present some preliminary fundamental results, and describe our initial work in constructing a P2P data management system.04/2001;
- [Show abstract] [Hide abstract]
ABSTRACT: If industry visionaries are correct, our lives will soon be full of sensors, connected together in loose conglomerations via wireless networks, each monitoring and collecting data about the environment at large. These sensors behave very differently from traditional database sources: they have intermittent connectivity, are limited by severe power constraints, and typically sample periodically and push immediately, keeping no record of historical information. These limitations make traditional database systems inappropriate for queries over sensors. We present the Fjords architecture for managing multiple queries over many sensors, and show how it can be used to limit sensor resource demands while maintaining high query throughput. We evaluate our architecture using traces from a network of traffic sensors deployed on Interstate 80 near Berkeley and present performance results that show how query throughput, communication costs and power consumption are necessarily coupled in sensor environmentsData Engineering, 2002. Proceedings. 18th International Conference on; 02/2002
- [Show abstract] [Hide abstract]
ABSTRACT: In many recent applications, data may take the form of continuous data streams, rather than finite stored data sets. Several aspects of data management need to be reconsidered in the presence of data streams, offering a new research direction for the database community. In this paper we focus primarily on the problem of query processing, specifically on how to define and evaluate continuous queries over data streams. We address semantic issues as well as efficiency concerns. Our main contributions are threefold. First, we specify a general and flexible architecture for query processing in the presence of data streams. Second, we use our basic architecture as a tool to clarify alternative semantics and processing techniques for continuous queries. The architecture also captures most previous work on continuous queries and data streams, as well as related concepts such as triggers and materialized views. Finally, we map out research topics in the area of query processing over data streams, showing where previous work is relevant and describing problems yet to be addressed.ACM SIGMOD Record 09/2001; 30:109-120. · 0.96 Impact Factor