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: A novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages, which has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency.
89 citations
Patent•
12 Aug 2009TL;DR: In this article, a plurality of data management policies from a user are stored in a memory in association with a context and an identifier of the user, and a centralized interface is provided to the user for managing the personal data stored in the memory.
Abstract: Receive a plurality of data management policies from a user. Store in a memory the data management policies in association with a context and an identifier of the user. Receive personal data of the user collected in one or more contexts by one or more collection devices. Determine whether the personal data complies with one or more of the data management policies with respect to collecting personal data of the user. If the personal data complies with the data management policies with respect to collecting personal data on the user, then storing in the memory the personal data in association with the identifier of the user. Provide a centralized interface to the user for managing the personal data stored in the memory.
89 citations
TL;DR: A comprehensive survey of existing data mining techniques and their multilevel classification scheme is presented and an adaptive data mining framework of WSNs for future research is proposed.
Abstract: Recently, data management and processing for wireless sensor networks (WSNs) has become a topic of active research in several fields of computer science, such as the distributed systems, the database systems, and the data mining. The main aim of deploying the WSNs-based applications is to make the real-time decision which has been proved to be very challenging due to the highly resource-constrained computing, communicating capacities, and huge volume of fast-changed data generated by WSNs. This challenge motivates the research community to explore novel data mining techniques dealing with extracting knowledge from large continuous arriving data from WSNs. Traditional data mining techniques are not directly applicable to WSNs due to the nature of sensor data, their special characteristics, and limitations of the WSNs. This work provides an overview of how traditional data mining algorithms are revised and improved to achieve good performance in a wireless sensor network environment. A comprehensive survey of existing data mining techniques and their multilevel classification scheme is presented. The taxonomy together with the comparative tables can be used as a guideline to select a technique suitable for the application at hand. Based on the limitations of the existing technique, an adaptive data mining framework of WSNs for future research is proposed.
89 citations
Patent•
17 Aug 2012
TL;DR: In this paper, a system is provided for qualifying and analyzing data for at least one business intelligence, where a data management system transforms raw data and stores it, and an analytic engine is included.
Abstract: A system is provided for qualifying and analyzing data for at least one business intelligence. A platform receives source data. A data management system transforms raw data and stores it. An analytic engine is included. In operation the data management system receives first, second, and third streams of source data. The first stream is client source data, the second stream is public source data and the third stream is acquired by the data management system. The data management system organizes the first, second and third streams of data into items and their attributes. The analytic engine receives the items with their attributes from the data management system and applies logic to provide multi-dimensional analysis relative to a scale for at least one business intelligence.
89 citations
TL;DR: This paper aims to present a generalized view of complete big data system which includes several stages and key components of each stage in processing the big data, and systematically investigates big data tools and technologies including distributed/cloud-based stream processing tools in a comparative approach.
Abstract: The traditional databases are not capable of handling unstructured data and high volumes of real-time datasets Diverse datasets are unstructured lead to big data, and it is laborious to store, manage, process, analyze, visualize, and extract the useful insights from these datasets using traditional database approaches However, many technical aspects exist in refining large heterogeneous datasets in the trend of big data This paper aims to present a generalized view of complete big data system which includes several stages and key components of each stage in processing the big data In particular, we compare and contrast various distributed file systems and MapReduce-supported NoSQL databases concerning certain parameters in data management process Further, we present distinct distributed/cloud-based machine learning (ML) tools that play a key role to design, develop and deploy data models The paper investigates case studies on distributed ML tools such as Mahout, Spark MLlib, and FlinkML Further, we classify analytics based on the type of data, domain, and application We distinguish various visualization tools pertaining three parameters: functionality, analysis capabilities, and supported development environment Furthermore, we systematically investigate big data tools and technologies (Hadoop 30, Spark 23) including distributed/cloud-based stream processing tools in a comparative approach Moreover, we discuss functionalities of several SQL Query tools on Hadoop based on 10 parameters Finally, We present some critical points relevant to research directions and opportunities according to the current trend of big data Investigating infrastructure tools for big data with recent developments provides a better understanding that how different tools and technologies apply to solve real-life applications
88 citations