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In this paper, we introduce a novel spatial query called the view field nearest neighbor query. Given the view field and location of a user, the view field nearest neighbor query retrieves a data object that is nearest to the user’s location and falls within the user’s view field. This query can be employed for applications such as augmented reality systems, tour guide systems, and CCTV-based surveillance systems. We propose a view field nearest neighbor query processing method that considers moving data objects (i.e., continuous view field nearest neighbor query processing), where we utilize the grid index. Continuous view field nearest neighbor query processing consists of two phases: (1) initial phase and (2) update phase. The first phase computes the initial result of a view field nearest neighbor query (i.e., snapshot query result) and the second phase continuously updates the result according to the movement of data objects. For the initial phase, we propose two algorithms: Naive Exploration Algorithm and Fan-shaped Exploration Algorithm. For the update phase, we propose the Fan-shaped Monitoring Algorithm to process the moving data objects efficiently. Through extensive experiments, we investigate the performance of our proposed algorithms on synthetic and real data sets.

The increasing volumes of relational data let us find an alternative to cope with them. Recently, several hybrid approaches (e.g., HadoopDB and Hive) between parallel databases and Hadoop have been introduced to the database community. Although these hybrid approaches have gained wide popularity, they cannot avoid the choice of suboptimal execution strategies. We believe that this problem is caused by the inherent limits of their architectures. In this demo, we present Tajo, a relational, distributed data warehouse system on shared-nothing clusters. It uses Hadoop Distributed File System (HDFS) as the storage layer and has its own query execution engine that we have developed instead of the MapReduce framework. A Tajo cluster consists of one master node and a number of workers across cluster nodes. The master is mainly responsible for query planning and the coordinator for workers. The master divides a query into small tasks and disseminates them to workers. Each worker has a local query engine that executes a directed acyclic graph of physical operators. A DAG of operators can take two or more input sources and be pipelined within the local query engine. In addition, Tajo can control distributed data flow more flexible than that of MapReduce and supports indexing techniques. By combining these features, Tajo can employ more optimized and efficient query processing, including the existing methods that have been studied in the traditional database research areas. To give a deep understanding of the Tajo architecture and behavior during query processing, the demonstration will allow users to submit TPC-H queries to 32 Tajo cluster nodes. The web-based user interface will show (1) how the submitted queries are planned, (2) how the query are distributed across nodes, (3) the cluster and node status, and (4) the detail of relations and their physical information. Also, we provide the performance evaluation of Tajo compared with Hive.

RDF is a data model for representing labeled directed graphs, and it is used as an important building block of semantic web. Due to its flexibility and applicability, RDF has been used in applications, such as semantic web, bioinformatics, and social networks. In these applications, large-scale graph datasets are very common. However, existing techniques are not effectively managing them. In this paper, we present a scalable, efficient query processing system for RDF data, named SPIDER, based on the well-known parallel/distributed computing framework, Hadoop. SPIDER consists of two major modules (1) the graph data loader, (2) the graph query processor. The loader analyzes and dissects the RDF data and places parts of data over multiple servers. The query processor parses the user query and distributes sub queries to cluster nodes. Also, the results of sub queries from multiple servers are gathered (and refined if necessary) and delivered to the user. Both modules utilize the MapReduce framework of Hadoop. In addition, our system supports some features of SPARQL query language. This prototype will be foundation to develop real applications with large-scale RDF graph data.

In this paper, we discuss the top-k monitoring over sensor networks. Since sensor readings are usually correlated with location, top-k nodes are clustered at some areas. Motivated by such a characteristic, we propose a novel tree structure named partial ordered tree(POT) to efficiently maintain clusters of the highest readings. By using POTs, only candidate nodes which might be included in top-k result are evaluated for query processing. Through simulation experiments, we evaluate the performance of the POT method in comparison with conventional methods.