Topic
Data management
About: Data management is a research topic. Over the lifetime, 31574 publications have been published within this topic receiving 424326 citations.
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TL;DR: In this paper, the authors report the findings of a recent Australian study of performance management systems conducted by the School of Management at Curtin University of Technology, Perth, in association with t...
Abstract: This paper reports the findings of a recent Australian study of performance management systems conducted by the School of Management at Curtin University of Technology, Perth, in association with t...
117 citations
TL;DR: The proposed system realizes lightweight data encryption, lightweight keyword trapdoor generation and lightweight data recovery, which leaves very few computations to user's terminal, and requires much less communication cost.
117 citations
TL;DR: Four more specific models including time-sensitive model, distributed model, hierarchical and multi-dimensional model, and skewed data model are introduced as an extension of the general data stream model.
Abstract: In many real-world applications, information such as web click data, stock ticker data, sensor network data, phone call records, and traffic monitoring data appear in the form of data streams. Online monitoring of data streams has emerged as an important research undertaking. Estimating the frequency of the items on these streams is an important aggregation and summary technique for both stream mining and data management systems with a broad range of applications. This paper reviews the state-of-the-art progress on methods of identifying frequent items from data streams. It describes different kinds of models for frequent items mining task. For general models such as cash register and Turnstile, we classify existing algorithms into sampling-based, counting-based, and hashing-based categories. The processing techniques and data synopsis structure of each algorithm are described and compared by evaluation measures. Accordingly, as an extension of the general data stream model, four more specific models including time-sensitive model, distributed model, hierarchical and multi-dimensional model, and skewed data model are introduced. The characteristics and limitations of the algorithms of each model are presented, and open issues waiting for study and improvement are discussed.
117 citations
TL;DR: This paper presents a prototype implementation of an agricultural process-data service (APDS), part of an infrastructure for data management in information-driven plant production, developed in the Pre agro joint research project.
117 citations