Titlebook: Data Management in Pervasive Systems; Francesco Colace,Massimo De Santo,Letizia Tanca Book 2015 Springer International Publishing Switzerl

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書目名稱Data Management in Pervasive Systems影響因子(影響力)




書目名稱Data Management in Pervasive Systems影響因子(影響力)學(xué)科排名




書目名稱Data Management in Pervasive Systems網(wǎng)絡(luò)公開度




書目名稱Data Management in Pervasive Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Management in Pervasive Systems被引頻次




書目名稱Data Management in Pervasive Systems被引頻次學(xué)科排名




書目名稱Data Management in Pervasive Systems年度引用




書目名稱Data Management in Pervasive Systems年度引用學(xué)科排名




書目名稱Data Management in Pervasive Systems讀者反饋




書目名稱Data Management in Pervasive Systems讀者反饋學(xué)科排名

慢慢啃

Constrain

https://doi.org/10.1007/978-3-642-84499-7is chapter, we will review the current state of the art of content-based multimedia information retrieval, including the most promising browsing and search paradigms for the several types of multimedia data, and show some cultural heritage applications.

inchoate

Data Streams and Data Stream Management Systems and Languagesstream management systems (DSMSs) are as follows: (a) results must be pushed to the output promptly and eagerly while input tuples continue to arrive and (b) because of the unbounded and massive nature of data streams, all past tuples cannot be memorized for future use. Only synopses can be kept in memory and the rest must be discarded.

Fallibility

Book 2015ring for personalization and recommendation.The book is organized into the following main parts: i) multimedia information management; ii) sensor data streams and storage; iii) social networks as information sources; iv) context awareness and personalization. The case study is used throughout the book as a reference example.

Control-Group

Control-Group

Data Personalization personalization, such as serendipity and diversity, and we will present representative methods that achieve personalized results of different properties. Then, we will describe how preferences, either at the group or the individual level can be modelled, and we will conclude the chapter with an overview of user preference learning methods.

悲痛

David A. Rosenbaum,Edward Saltzman touch with everything and everywhere, thus closing the “circle of pervasiveness.” The Internet of the future promises to connect our mobile devices with everything (from the fridge in our homes to special sensors in our cars or even in our bodies), whenever and whereverwe are.

faculty

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