Data management

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

An integrative framework for IS quality management

Abstract: T he importance of information technologies and the information systems function is no longer of debate among business people. The question, rather, is how an organization can take best advantage of IT in order to support its operations, add value to its products and services, and gain a competitive edge in the marketplace. To be able to perform up to such high expectations, the IS function must develop an intimate understanding of the expectations of its varied clientele. As organizations embark on their journey to be more responsive to their customers and to continuously improve the quality of their products and services, IS must do the same. Unfortunately, it seems that despite the importance of IT to the success of most organizations, the function is not proactive when it comes to actively pursuing and implementing quality principles. Surveys of IS managers [5] found that a minority of IS managers (41%) understood the basic principles of Total Quality Management (TQM), and thought they would be useful to the IS function. Even in the cases where TQM principles were understood, they often were not implemented in the IS function. Previous research in this area is at best fragmented, with focus on only subsets of IS quality (software and data quality), and is centered around front-line (core) processes (for example, systems development). This article takes a holistic view of quality in the IS function from the perspective of an IS manager and considers issues relating to multiple stakeholder groups, product, service, and process quality. This framework represents a contribution to both the practice as well as the research of how the IS function should be managed. From the practical perspective, the framework along with the discussion of many issues relating to the implementation and management of an IS quality system can be used to introduce and pursue a philosophy of total IS quality. From the research perspective, the framework integrates and fills in gaps in the existing literature, and it proposes new research questions. Total IS quality is a multidimensional Considering issues relating to multiple stakeholder groups, product, service, and process quality is important for managing IS quality. ■ Antonis C. Stylianou and Ram L. Kumar

Automated traffic management system and method

Abstract: A data management system and method that enables acquisition, integration and management of real-time data generated at different rates, by multiple, heterogeneous incompatible data sources. The system achieves this functionality by using an expert system to fuse data from a variety of airline, airport operations, ramp control, and air traffic control tower sources, to establish and update reference data values for every aircraft surface operation. The system may be configured as a real-time airport surface traffic management system (TMS) that electronically interconnects air traffic control, airline data and airport operations data to facilitate information sharing and improve taxi queuing. In the TMS operational mode, empirical data shows substantial benefits in ramp operations for airlines, reducing departure taxi times by about one minute per aircraft in operational use, translating as $12 to $15 million per year savings to airlines at the Atlanta, Georgia airport. The data management system and method may also be used for scheduling the movement of multiple vehicles in other applications, such as, marine vessels in harbors and ports, trucks or railroad cars in ports or shipping yards, and railroad cars in switching yards. Finally, the data management system and method may be used for managing containers at a shipping dock, stock on a factory floor or in a warehouse, or as a training tool for improving situational awareness of FAA tower controllers, ramp and airport operators or commercial airline personnel in airfield surface operations.

Composition of mappings given by embedded dependencies

Abstract: Composition of mappings between schemas is essential to support schema evolution, data exchange, data integration, and other data management tasks. In many applications, mappings are given by embedded dependencies. In this paper, we study the issues involved in composing such mappings.

Big Data Analysis Using Apache Hadoop

Abstract: We live in on-demand, on-command Digital universe with data prolifering by Institutions, Individuals and Machines at a very high rate. This data is categories as "Big Data" due to its sheer Volume, Variety and Velocity. Most of this data is unstructured, quasi structured or semi structured and it is heterogeneous in nature. The volume and the heterogeneity of data with the speed it is generated, makes it difficult for the present computing infrastructure to manage Big Data. Traditional data management, warehousing and analysis systems fall short of tools to analyze this data. Due to its specific nature of Big Data, it is stored in distributed file system architectures. Hadoop and HDFS by Apache is widely used for storing and managing Big Data. Analyzing Big Data is a challenging task as it involves large distributed file systems which should be fault tolerant, flexible and scalable. Map Reduce is widely been used for the efficient analysis of Big Data. Traditional DBMS techniques like Joins and Indexing and other techniques like graph search is used for classification and clustering of Big Data. These techniques are being adopted to be used in Map Reduce. In this paper we suggest various methods for catering to the problems in hand through Map Reduce framework over Hadoop Distributed File System (HDFS). Map Reduce is a Minimization technique which makes use of file indexing with mapping, sorting, shuffling and finally reducing. Map Reduce techniques have been studied in this paper which is implemented for Big Data analysis using HDFS.

Requirements for cloud-based BIM governance solutions to facilitate team collaboration in construction projects

Abstract: Construction projects involve multi-disciplinary and multi-actor collaborations that generate massive amounts of data over their lifecycle. Data are often sensitive, and embody rights, ownership and intellectual property of the creator. Managing project information raises concerns about security, inconsistency and loss of data. Conventional approach of dealing with the complexities of data management involves the adoption of BIM based solutions that lack suitable means for the governance of collaboration, and access and archival of managed data. To overcome the limitations of BIM, Cloud-based governance solutions have been suggested as a way forward. However, there is a lack of understanding of construction ICT (Information and Communication Technology) practices from the perspectives of data management and governance. This paper aims to fill this gap; first, by exploring barriers related to BIM adoption and collaboration practices, in particular, issues related to data management and governance that can potentially be ameliorated with Cloud technologies, and second, by identifying key requirements for Cloud-based BIM governance solutions. A structured questionnaire was conducted among informed construction practitioners in this study. The findings reveal several barriers to BIM adoption alongside ICT and collaboration issues with an urgent need to develop a BIM governance solution underpinned by cloud technology. Further, a number of important requirements for developing BIM governance solutions have been identified.