A signature-based Grid index design for main-memory RFID database applications

Suei, Pei-Lun; Lu, Yung-Feng; Liao, Rong-Jhang; Lo, Shi-Wu

doi:10.1016/j.jss.2012.01.026

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…A number of successful experiments with different tree indexes have been done. Another good optimization approach based on index tables is presented in [16]. It enables generating effective SQL queries.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Analysis of a Relational Database for Behavioral Experiments Data Processing

et al. 2018

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Abstract-This paper presents the results of a comparative analysis between different approaches to experimental data storage and processing. Several studies related to the problem and some methods for solving it have been discussed. Different types of databases, ways of using them and the areas of their application are analyzed. For the purposes of the study, a relational database for storing and analyzing specific data from behavioral experiments was designed. The methodology and conditions for conducting the experiments are described. Three different indicators are analyzed, respectively: memory required to store the data, time to load the data from an external file into computer memory and iteration time across all records through one cycle. The obtained results show that for storing a large number of records (in the order of tens of millions rows) either dynamic arrays (stored on external media in binary file format), or an approach based on a local or remote database management system can be used. Regarding the data loading time, the fastest approach was the one that uses dynamic arrays. It outperforms significantly the approaches based on a local or remote database. The obtained results show that the dynamic arrays and the local data sets approaches iterated much faster across all data records than the remote database approach.

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Section: Literature Reviewmentioning

confidence: 99%

Design and Analysis of a Relational Database for Behavioral Experiments Data Processing

et al. 2018

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“…There are many excellent works on relational-database (RMDB), e.g., COTS DBMS (Suei et al 2010, Chaudhuri et al 2014), H2DB (Suei et al 2012). However, it costs conventional, relational-database (RMDB) management systems a great deal to maintain its characteristics of parallel processing, thus affecting throughput performance in processing real-time data.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Data Filter for Interactive-TV Rating System

Wang

Chen

et al. 2015

AMM

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The set-top box (STB) connected to TVs are used to gather viewing data, which then is transmitted to a backend platform. It is very hard to analyze the viewing behavior since the viewing data are very huge and contain noise. This paper presents a real-time interactive-TV rating system for monitoring the trend of user viewing behavior. The system collects viewing behavior data from CATV system vendors, analyzes the data, and then provides real-time analysis diagrams. The authors also proposed two phases filtering algorithm that can efficiently check and filter the invalid viewing behavior according to the effective data filter rules. The algorithm can daily filter 27 million viewing behavior data and generate 7.6 million effective data. It can reduce the need of storage space and increase the analysis speed. In this works, we develop a real-time interactive-TV rating system for monitoring the trend of user viewing behavior and provide the real-time ratings for the last three minutes watched.

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