Data management

About: Data management is a research topic. Over the lifetime, 31574 publications have been published within this topic receiving 424326 citations.

Smart Grid Data Cloud: A Model for Utilizing Cloud Computing in the Smart Grid Domain

Abstract: This paper presents a model for smart grid data management based on specific characteristics of cloud computing, such as distributed data management for real-time data gathering, parallel processing for real-time information retrieval, and ubiquitous access. The appliance of the cloud computing model meets the requirements of data and computing intensive smart grid applications. We gathered these requirements by analyzing the set of well-known smart grid use cases, most of which demand flexible collaboration across organizational boundaries of network operators and energy service providers as well as the active participation of the end user. Hence, preserving confidentiality and privacy, whilst processing the massive amounts of smart grid data, is of paramount importance in the design of the proposed Smart Grid Data Cloud.

Considerations for big data: Architecture and approach

Abstract: The amount of data in our industry and the world is exploding. Data is being collected and stored at unprecedented rates. The challenge is not only to store and manage the vast volume of data (“big data”), but also to analyze and extract meaningful value from it. There are several approaches to collecting, storing, processing, and analyzing big data. The main focus of the paper is on unstructured data analysis. Unstructured data refers to information that either does not have a pre-defined data model or does not fit well into relational tables. Unstructured data is the fastest growing type of data, some example could be imagery, sensors, telemetry, video, documents, log files, and email data files. There are several techniques to address this problem space of unstructured analytics. The techniques share a common character tics of scale-out, elasticity and high availability. MapReduce, in conjunction with the Hadoop Distributed File System (HDFS) and HBase database, as part of the Apache Hadoop project is a modern approach to analyze unstructured data. Hadoop clusters are an effective means of processing massive volumes of data, and can be improved with the right architectural approach.

Facilities Management: Theory and Practice

Abstract: Foreword. Preface. Facilities management. Organization and management. Facilities management skills. Professional practice. Total quality management. Value management. Risk management. Building performance. Environmental management. Information management. Support services. Project management. Index.

System for moving real-time data events across a plurality of devices in a network for simultaneous data protection, replication, and access services

Abstract: A data management system or “DMS” provides a wide range of data services to data sources associated with a set of application host servers. The data management system typically comprises one or more regions, with each region having one or more clusters. A given cluster has one or more nodes that share storage. To facilitate the data service, a host driver embedded in an application server connects an application and its data to a cluster. The host driver provides a method and apparatus for capturing real-time data transactions in the form of an event journal that is provided to the data management system. The driver functions to translate traditional file/database/block I/O into a continuous, application-aware, output data stream. Using the streams generated in this manner, the DMS offers a wide range of data services that include, by way of example only: data protection (and recovery), disaster recovery (data distribution and data replication), data copy, and data query and access.

On-To-Knowledge Methodology (OTKM)

Abstract: In this chapter we present the On-To-Knowledge Methodology (OTKM) for introducing and maintaining ontology based knowledge management applications into enterprises with a focus on Knowledge Processes and Knowledge Meta Processes. While the former process circles around the usage of ontologies, the latter process guides their initial set up. We illustrate our methodology by an example from a case study on skills management.