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Massive knowledge graphs, such as Linked Open Data or Freebase, contain billions of labeled entities and relationships. Star queries aim to identify an entity given a set of related entities, and they are common with massive knowledge graphs. It is important to find the best way to answer star queries, and we can do this by treating it as a graph pattern-matching problem. Because knowledge graphs...
With the rapid development of information technology, enormous volumes of data is being generated by many enterprises at all times. A reasonable storage of these large scale data to reduce cost and achieve internal data sharing and collaboration has always been a challenge for enterprises. Cloud storage technology, as an important branch in the field of cloud computing, is becoming a trend to solve...
Massive information networks, such as the knowledge graph by Google, contain billions of labeled entities. Star queries, which aim to identify an entity, given a set of related entities, are common on such networks. Answering star queries can be modeled as a graph pattern matching problem. Traditional approaches apply graph indices to accelerate the query processing. Unfortunately, it is so costly...
Data-intensive services have become one of the most challenging applications in cloud computing. The classical service composition problem will face new challenges as the services and correspondent data grow. A typical environment is the large scale scientific project AMS, which we are processing huge amount of data streams. In this paper, we will resolve service composition problem by considering...
With the explosion of data in the past decade, big data is becoming a research hotspot in the information field. Many cloud-based distributed data processing platforms have been proposed to provide efficient and cost effective solutions for big data query processing, such as Hadoop, Hive, Pig, etc. However, most of the current research works are focus on improving the performance of query processing...
Hadoop as a popular open-source implementation of MapReduce is widely used for large scale data-intensive applications like data mining, web indexing and scientific computing. The current Hadoop implementation assumes that nodes in a cluster are homogeneous in nature, and Hadoop distributed file system(HDFS) distributes data to multiple nodes based on disk space availability. Such data placement strategy...
Running data-intensive scientific workflow across multiple data centers faces massive data transfer problem which leads to low efficiency in actual workflow application for scientists. By considering data size and data dependency, we propose a k-means algorithm based initial data placement strategy that places the most related initial data sets into the same data center at workflow preparation stage...
Virtualization based cloud and big data applications have been widely adopted in various fields. Because deploying the big data applications on the cloud will cause obvious performance degradation, the cloud and big data applications are provided with fixed resource separately. However, the traditional fixed resource allocation mechanism has two drawbacks: (1) low resource utility and (2) unresponsiveness...
Huge collections of data have been created in recent years. Cloud computing provides a way to enable massive amounts of data to work together as data-intensive services. Considering Big Data and the cloud together, which is a practical and economical way to deal with Big Data, will accelerate the availability and acceptability of analysis of the data. Providing an efficient mechanism for optimized...
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