Abstract:In data-intensive real-time applications, e.g., cognitive assistance and mobile health (mHealth), the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., mental or physical health conditions, from sensor data streams in real-time rather than overloading users with massive raw data. However, achieving the objective is challenging due to the data volume and complex data analysis tasks with stringent timing constraints. Most existing big data management systems, e.g., Hadoop, are not directly applicable to real-time sensor data analytics, since they are timing agnostic and focus on batch processing of previously stored data that are potentially outdated and subject to I/O overheads. Moreover, embedded sensors and IoT devices lack enough resources to perform sophisticated data analytics. To address the problem, we design a new real-time big data management framework to support periodic in-memory real-time sensor data analytics at the network edge by extending the map-reduce model originated in functional programming, while providing adaptive sensor data transfer to the edge server based on data importance. In this paper, a prototype system is designed and implemented as a proof of concept. In the performance evaluation, it is empirically shown that important sensor data are delivered in a preferred manner and they are analyzed in a timely fashion.

No abstract

No abstract

Well-designed indices can dramatically improve query performance. Including query workload information can produce indices that yield better overall throughput while balancing the space and performance trade-off at the core of index design. In the context of XML, structural indices have proven to be particularly effective in supporting XPath queries by capturing the structural correlation between data components in an XML document. In this paper, we propose a family of novel workload-aware indices by taking advantage of the disk-based P[k]-Trie index framework, which indexes node pairs of an XML document to facilitate indexonly evaluation plans. Our indices are designed to be optimal for answering frequent path queries in one index lookup and efficient for answering non-frequent path queries using an index-only plan. Experimental results prove that our indices outperform the APEX index in overall throughput and excel in answering non-frequent queries, queries with predicates, and queries that yield empty results.