People, devices, infrastructures and sensors can constantly communicate exchanging data and generating new data that trace many of these exchanges. This leads to vast volumes of data collected at ever increasing veloc...
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People, devices, infrastructures and sensors can constantly communicate exchanging data and generating new data that trace many of these exchanges. This leads to vast volumes of data collected at ever increasing velocities and of different variety, a phenomenon currently known as bigdata. In particular, recent developments in Information and Communications Technologies are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining volume, variety and velocity of data, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the Future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being bigdata systems needed to link all the entities and data needs of the factory. Thereby, this paper addresses this key challenge, proposing and implementing a bigdata Analytics architecture, using a multinational organisation (Bosch Car Multimedia - Braga) as a case study. In this work, all the data lifecycle, from collection to analysis, is handled, taking into consideration the different data processing speeds that can exist in the real environment of a factory (batch or stream).
To ensure green manufacturing, the energy consumption of production processes should be transparent and minimized. Also, to achieve the desired level of energy consumption awareness and efficiency improvements, energy...
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To ensure green manufacturing, the energy consumption of production processes should be transparent and minimized. Also, to achieve the desired level of energy consumption awareness and efficiency improvements, energy use should be measured in more detail and linked to production data. In this scenario, real-time monitoring of energy consumption represents an essential step to increasing energy awareness, efficiency and the support of energy-aware production processes. This paper seeks to provide a way to achieve multi-level awareness of the energy used during production processes. The multi-level awareness of energy consumption means identifying the amount of energy used, CO2 emitted, and the cost of the energy used at operation, product, and order level. This multi-level awareness is achieved by integrating energy usage data with production data at the operational level. Furthermore, energy sources need to be considered to define the amount of CO2 that is emitted from the production process for each product. A pilot study was carried out to integrate electrical energy data, production data and scheduling data in real time to achieve the multi-level awareness of energy used in production. The results show that integrating energy with production data enables factories to provide specific energy consumption information for decision makers at the factory level, as well as for the consumers and the regulators. This integration of energy and production data is achieved efficiently when there is a high level of standardization of production processes and the availability of detailed energy usage data. (C) 2016 Elsevier Ltd. All rights reserved.
The cloud is increasingly being used to store and process the bigdata. Many researchers have been trying to protect bigdata in cloud computing environment. Traditional security mechanisms using encryption are neithe...
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The cloud is increasingly being used to store and process the bigdata. Many researchers have been trying to protect bigdata in cloud computing environment. Traditional security mechanisms using encryption are neither efficient nor suited to the task of protecting bigdata in the Cloud. In this paper, we first discuss about challenges and potential solutions for protecting bigdata in cloud computing. Second, we proposed MetaClouddataStorage architecture for protecting bigdata in Cloud Computing Environment. This framework ensures that efficient processing of bigdata in cloud computing environment and gains more business insights.
Learning Analytics (LA) is currently the most effective way of achieving better information and in-depth insights of the learning processes. Specifications like Experience API (xAPI) have been defined as part of LA in...
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ISBN:
(纸本)9781509059201
Learning Analytics (LA) is currently the most effective way of achieving better information and in-depth insights of the learning processes. Specifications like Experience API (xAPI) have been defined as part of LA initiatives to add interoperability among LA-aware applications. Tools that validate the conformance of data to these specifications are key components to assure the interoperability among applications. In this paper, a comparative evaluation of relevant validation tools of the xAPI specification is presented. This comparison focus specially on the structural and semantic features of the xAPI specification, revealing that most of the currently available tools do not support the semantic constraints of the specification.
This paper shows the solutions developed in the Project "Sistema Innovativo bigdata Analytics" named SIBDA. The needs of the three involved companies are described, which led to define an overall framework ...
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This paper shows the solutions developed in the Project "Sistema Innovativo bigdata Analytics" named SIBDA. The needs of the three involved companies are described, which led to define an overall framework in the field of bigdata through application cases. The functional and technological requirements of an integrated big data architecture are given. In particular, the criteria for selecting the solutions for Document Management for one company (Microdata Service) are described. The resulting Enterprise Content Management (ECM) system architecture and the overall system architecture are given. SIBDA stands for Sistema Innovativo bigdata Analytics, a project funded by Regione Lombardia within "Accordi di Competitivita", involving three ICT companies (Mail Up s.p.a, Microdata Service and LineaCom), belonging to the CRIT Consortium, Cremona, and Politecnico di Milano.
To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especi...
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To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especially review volume and rating. However, limited attention has been paid to understand how other factors moderate the effect of product review on online sales. This study aims to confirm the importance of review volume and rating on improving sales performance, and further examine the moderating roles of product category, answered questions, discount and review usefulness in such relationships. By analyzing 2939 records of data extracted from *** using a big data architecture, it is found that review volume and rating have stronger influence on sales rank for search product than for experience product. Also, review usefulness significantly moderates the effects of review volume and rating on product sales rank. In addition, the relationship between review volume and sales rank is significantly moderated by both answered questions and discount. However, answered questions and discount do not have significant moderation effect on the relationship between review rating and sales rank. The findings expand previous literature by confirming important interactions between customer review features and other factors, and the findings provide practical guidelines to manage e-businesses. This study also explains a big data architecture and illustrates the use of bigdata technologies in testing theoretical framework. (C) 2016 Elsevier Ltd. All rights reserved.
Online-activity-generated digital traces provide opportunities for novel services and unique insights as demonstrated in, for example, research on mining software repositories. The inability to link these traces withi...
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ISBN:
(纸本)9781450341523
Online-activity-generated digital traces provide opportunities for novel services and unique insights as demonstrated in, for example, research on mining software repositories. The inability to link these traces within and among systems, such as Twitter, GitHub, or Reddit, inhibit the advances in this area. Furthermore, no single approach to integrate data from these disparate sources is likely to work. We aim to design Foreseer, an extensible framework, to design and evaluate identity matching techniques for public, large, and low accuracy operational data. Foreseer consists of three functionally independent components designed to address the issues of discovery and preparation, storage and representation, and analysis and linking of traces from disparate online sources. The framework includes a domain specific language for manipulating traces, generating insights, and building novel services. We have applied it in a pilot study of roughly 10TB of data from Twitter, Reddit, and StackExchange including roughly 6M distinct entities and, using basic matching techniques, found roughly 83,000 matches among these sources. We plan to add additional entity extraction and identification algorithms, data from other sources, and design tools for facilitating dynamic ingestion and tagging of incoming data on a more robust infrastructure using Apache Spark or another distributed processing framework. We will then evaluate the utility and effectiveness of the framework in applications ranging from identifying malicious contributors in software repositories to the evaluation of the utility of privacy preservation schemes.
A Knowledge Cube, or cube for short, is an intelligent and adaptive database instance capable of storing, analyzing, and searching data. Each cube is established based on semantic aspects, e.g., (1) Topical, (2) Conte...
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ISBN:
(纸本)9781479912926;9781479912933
A Knowledge Cube, or cube for short, is an intelligent and adaptive database instance capable of storing, analyzing, and searching data. Each cube is established based on semantic aspects, e.g., (1) Topical, (2) Contextual, (3) Spatial, or (4) Temporal. A cube specializes in handling data that is only relevant to the cube's semantics. Knowledge cubes are inspired by two prime architectures: (1) dataspaces that provides an abstraction for data management where heterogeneous data sources can co-exist and it requires no prespecified unifying schema, and (2) Linked data that provides best practices for publishing and interlinking structured data on the web. A knowledge cube uses Linked data as its main building block for its data layer and encompasses some of the data integration abstractions defined by dataspaces. In this paper, knowledge cubes are proposed as a semantically-guided data management architecture, where data management is influenced by the data semantics rather than by a predefined scheme. Knowledge cubes support the five pillars of bigdata also known as the five V's, namely Volume, Velocity, Veracity, Variety, and Value. Interesting opportunities can be leveraged when learning the semantics of the data. This paper highlights these opportunities and proposes a strawman design for knowledge cubes along with the research challenges that arise when realizing them.
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