3,720 research outputs found
Model-View Sensor Data Management in the Cloud
Infinite nature of sensor data poses a serious challenge for query processing even in a cloud infrastructure. Model-based sensor data approximation reduces the amount of data for query processing, but all modeled segments need to be scanned, in the worst case. In this paper, we propose an innovative index for modeled segments in key-value stores, namely KVI-index. KVI-index has an in-memory tree component and a secondary structure materialized in the key-value store that maps the tree nodes to the modeled data segments. Then, we introduce a KVI-index-Scan-MapReduce hybrid approach to perform efficient query processing. As proved by a series of experiments in a real private cloud infrastructure, our approach outperforms in query response time and index updating efficiency both Hadoop-based parallel processing of the raw sensor data and multiple alternative indexing approaches of model-view data
Efficient Indexing and Query Processing of Model-View Sensor Data in the Cloud
As the number of sensors that pervade our lives increases (e.g., environmental sensors, phone sensors, etc.), the efficient management of massive amount of sensor data is becoming increasingly important. The infinite nature of sensor data poses a serious challenge for query processing even in a cloud infrastructure. Traditional raw sensor data management systems based on relational databases lack scalability to accommodate large-scale sensor data efficiently. Thus, distributed key-value stores in the cloud are becoming a prime tool to manage sensor data. Model-view sensor data management, which stores the sensor data in the form of modeled segments, brings the additional advantages of data compression and value interpolation. However, currently there are no techniques for indexing and/or query optimization of the model-view sensor data in the cloud; full table scan is needed for query processing in the worst case. In this paper, we propose an innovative index for modeled segments in key-value stores, namely KVI-index. KVI-index consists of two interval indices on the time and sensor value dimensions respectively, each of which has an in-memory search tree and a secondary list materialized in the key-value store. Then, we introduce a KVI-index–Scan–MapReduce hybrid approach to perform efficient query processing upon modeled data streams. As proved by a series of experiments at a private cloud infrastructure, our approach outperforms in query-response time and index-updating efficiency both Hadoop-based parallel processing of the raw sensor data and multiple alternative indexing approaches of model-view data
EAGLE—A Scalable Query Processing Engine for Linked Sensor Data
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
Real-time analytics that requires integration and aggregation of
heterogeneous and distributed streaming and static data is a typical task in
many industrial scenarios such as diagnostics of turbines in Siemens. OBDA
approach has a great potential to facilitate such tasks; however, it has a
number of limitations in dealing with analytics that restrict its use in
important industrial applications. Based on our experience with Siemens, we
argue that in order to overcome those limitations OBDA should be extended and
become analytics, source, and cost aware. In this work we propose such an
extension. In particular, we propose an ontology, mapping, and query language
for OBDA, where aggregate and other analytical functions are first class
citizens. Moreover, we develop query optimisation techniques that allow to
efficiently process analytical tasks over static and streaming data. We
implement our approach in a system and evaluate our system with Siemens turbine
data
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