Exploiting compression and approximation paradigms for effective and efficient online analytical processing over sensor network readings in data grid environments

Abstract

Aggregate queries are useful tools in the context of sensor network-based systems as they retrieve knowledge from huge amounts of summarized readings to be exploited for knowledge discovery purposes. Actually, data representation and query models are problematic issues for managing sensor network data, because streams produced by sensors are theoretically unbounded. In this paper, we present a Grid framework, called SensorGrid, on the basis of data compression and approximation paradigms, which allows us to provide approximate answers to aggregate queries on summarized sensor network data. These queries are the basis for achieving Online Analytical Processing (OLAP) over sensor network readings in Data Grid environments, with both effectiveness and efficiency. We also present our experience in the context of a reallife system focused on the management of environmental sensor network data. Another contribution of our research is represented by the extensive experimental evaluation and analysis of SensorGrid, which, in more details, focuses on two main classes of aggregate range queries over sensor readings, namely, (i) the window queries, which apply an SQL aggregation operator over a fixed window over the reading stream produced by the sensor network, and (ii) the continuous queries, which instead consider a 'moving' window and produce as output a stream of answers. Both classes of queries are extremely useful to extract summarized knowledge to be exploited by OLAP-like analysis tools over sensor network data. The experimental results, conducted on both synthetic and real-life data sets, clearly confirm the benefits deriving from embedding data compression and approximation paradigms into Grid-based sensor network data-intensive management systems.This approach is very suitable for the novel Grid-based network applications paradigm [5], as Grid deploy model relies on the definition of strict protocols among Grid nodes, as well as the context of Grid Services (GS), which are made available to users and applications identified on the Grid. With respect to the classification of Bonnet et al. [1], our approach can be classified as a warehousing approach in which readings are collected and stored in a Grid framework, not on a single DPS. In some sense, the Grid framework plays the role of distributed DPS.Grid Data Warehouses (GDWH) [7][8][9][10][11][12][13], which can be reasonably considered the 'timely' research area of SensorGrid, combine data representation and management functionalities of consolidated Data Warehousing (DW) methodologies [14], such as data integration, data consolidation, data loading and refreshing, storage primitives, metadata management and indexing routines, with high performance and high reliability of Grid Computing [4,5].Sensor network data management and analysis, the main goal of SensorGrid, is a typical context where GDWH technology can effectively provide facilities during all the phases, ranging from sensor reading collection, storage, and processing to OLAP [3] query evaluation over ...

An Effective and Efficient MapReduce Algorithm for Computing BFS-Based Traversals of Large-Scale RDF Graphs

“…Future work is oriented towards the integration of our algorithms with Cloud-inspired frameworks devoted to the distributed management of large-scale data repositories via data compression metaphors (e.g., [82,83]). …”

A new approximate query engine based on intelligent capture and fast transformations of granulated data summaries

“…These two types of data synopses -samples and summaries -are to some extent combinable (Ramnarayan et al 2016). However, the solutions developed so far build summaries for predefined query configurations or, e.g., OLAP-specific scenarios (Cuzzocrea and Saccȧ 2013). This limits their usefulness for exploratory analytics, where -by default -it is hard to anticipate queries that will be executed (Nguyen and Nguyen 2005).…”

Cloud Supported Building Data Analytics

“…Examples include an integration architecture of Cloud computing and WSNs [8], SensorWeb [9], SensorGrid [10], [11], [12], the Sensor-Cloud infrastructure [13], the BodyCloud architecture [16] and use of wireless sensors in buildings [17] etc.…”

“…Aggregate queries are the basis for achieving Online Analytical Processing (OLAP) over sensor network readings in Data Grid environments. OLAP has a number of interesting applications for eScience, covering aspects such as visualization of scientific data, multi-dimensional analysis of data streams, privacy of multi-dimensional data [10], [11], [12], etc.…”