Which method does openpds use to protect user privacy of gps records on a mobile device?

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The energy domain currently struggles with radical legal and technological changes, such as, smart meters. This results in new use cases which can be implemented based on business process technology. Understanding and automating business processes requires to model and test them. However, existing process testing approaches frequently struggle with the testing of process resources, such as ERP systems, and negative testing. Hence, this work presents a toolchain which tackles that limitations. The approach uses an open source process engine to generate event logs and applies process mining techniques in a novel way.

The Dagstuhl Seminar, “Social Issues in Computational Transportation Science” (13512) took place from 15 to 19 December 2103, attracting 27 participants active in a wide range of academic, commercial, and public sector areas. CTS is an emerging discipline that combines advances in computer science and engineering with the modeling, planning, social, and economic aspects of transportation in order to improve the safety, mobility, and sustainability of transportation systems. The aim of this seminar was to focus on the social computing aspects of CTS, including such areas as social networks and crowd-sourcing for transportation, as well as the integration of persuasive technologies and behavioral economics in social computing. In their time at the workshop, participants discussed and debated these and other topics, as shown in the workshop’s summary report.

Data has evolved into a large-scale data as big data in the recent era. The analysis of big data involves determined attempts on previous data. As new era of data has spatiotemporal facts that involve the time and space factors, which make them distinct from traditional data. The big data with spatiotemporal aspects helps achieve more efficient results and, therefore, many different types of frameworks have been introduced in cooperate world. In the present research, a qualitative approach is used to present the framework classification in two categories: architecture and features. Frameworks have been compared on the basis of architectural characteristics and feature attributes as well. These two categories project a significant effect on the execution of spatiotemporal data in big data. Frameworks are able to solve the real-time problems in less time of cycle. This study presents spatiotemporal aspects in big data with reference to several dissimilar environments and frameworks.

Crowdsourced multimedia data poses several challenges when it is collected, stored, indexed, retrieved, and visualized. Examples of crowd source multimedia data are social sensors, vehicle sensors, physical sensors, human sensors, etc. Analyzing such multimodal and diversified crowdsourced data provides very rich understanding about the need of individuals within a crowd. Such understanding makes it possible to tailor services to individuals’ needs, also called context-aware services. In this paper, we propose a spatial multimedia big data framework that can collect multimedia data from 1) a very large crowd equipped with multi-sensory smartphones, 2) vehicles, and 3) social networks. A set of multimedia services are offered to users to support their spatio-temporal activities. These include but not limited to 1) simple user interfaces to utilize multimedia services for instant guidance, 2) navigation to points of interests (POI), and 3) efficient and cost effective intra-city rides to users. The big data framework is designed to handle a very large number of multimedia spatio-temporal queries in real-time. The system is a pilot project and will be deployed during the event of Hajj 2015 when over three million pilgrims from all over the world will visit Makkah, Saudi Arabia to perform their Hajj rituals.

The Internet of Things, crowdsourcing, social media, public authorities, and other sources generate bigger and bigger data sets. Big and open data offers many benefits for emergency management, but also pose new challenges. This chapter will review the sources of big data and their characteristics. We then discuss potential benefits of big data for emergency management along with the technological and societal challenges it poses. We review central technologies for big-data storage and processing in general, before presenting the Spark big-data engine in more detail. Finally, we review ethical and societal threats that big data pose.

03/11/2018Chapter 1 Quiz: 2018-IOT FUNDAMENTALS: BIG DATA & ANALYTICS-ESCOM-T27In the data analysis process, which sequence depicts the work flow suitablefor data at rest?

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9/15It is a free version of RDBMS suitable for enterprises.It is a fully functional RDBMS for distributed data processing.Refer to curriculum topic: 1.3.2SQLite is an embedded SQL database engine in that it does notfollow the traditional client/server model like SQL RDBMS (relationaldatabase management system). SQLite reads and writes directly toordinary disk files.

2 / 2 ptsQuestion 13Which statement describes SQLite?Correct!Correct!

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