Titlebook: Database Systems for Advanced Applications; 19th International C Sourav S. Bhowmick,Curtis E. Dyreson,Bernhard Thal Conference proceedings

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書目名稱Database Systems for Advanced Applications讀者反饋




書目名稱Database Systems for Advanced Applications讀者反饋學(xué)科排名

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Online Indexing and Distributed Querying Model-View Sensor Data in the Cloudof sensor data becomes increasingly important at present. Traditional sensor data management systems based on relational database lack scalability to accommodate large-scale sensor data efficiently. Consequently, distributed key-value stores in the cloud is becoming the prime tool to manage sensor d

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Discovery of Areas with Locally Maximal Confidence from Location Dataough location data obtained from GPS-equipped devices have promising applications, those GPS points are usually not uniformly distributed in two-dimensional space. As a result, substantial insights might be missed by using data mining algorithms that discover admissible or rectangular areas under th

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竊喜

Scalable Numerical Queries by Algebraic Inequality Transformations readings into a relational database system. SQL queries over such log data often involve numerical conditions containing inequalities, e.g. to find suspected deviations from normal behavior based on some function over measured sensor values. However, such queries are often slow to execute, because

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Concurrent Execution of Mixed Enterprise Workloads on In-Memory Databasesve, both application classes issue a database workload with commonly agreed characteristics. However, traditional database management systems (DBMS) are typically optimized for one or the other. Today, we see two trends in enterprise applications that require bridging these two workload categories:

Cardioplegia

On Data Partitioning in Tree Structure Metric-Space Indexesd pivots). There are two basic forms of data partitioning: ball partition and General Hyper-plane (GH) partition. Most existing work only shows their superiority experimentally, and little theoretical proof is found. We propose an approach to unify existing data partitioning methods and analyze thei

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