There is an increasing interest in uncertain and probabilistic databases arising in application domains such as sensor networks, information retrieval, mobile object data management, information extraction, and data integration. A range of different approaches have been proposed to find the frequent items in uncertain database. But there is little work on processing such query in distributed, in-network inference, such as sensor network. In sensor network, communication is the primary problem because of limited batteries. In this paper, a synopsis with minimum amount tuples is proposed, which sufficient for answering the top-k query. And this synopsis can be dynamic maintained with new tuples been added. A novel communication efficient algorithm is presented in taking advantage of this synopsis. The test results confirm the effectiveness and efficiency of our approaches.
[Show abstract][Hide abstract] ABSTRACT: Count queries in wireless sensor networks (WSNs) report the number of sensor nodes whose measured values satisfy a given predicate.
However, measurements in WSNs are typically imprecise due, for instance, to limited accuracy of the sensor hardware. In this
context, we present four algorithms for computing continuous probabilistic count queries on a WSN, i.e., given a query Q we compute a probability distribution over the number of sensors satisfying Q’s predicate. These algorithms aim at maximizing the lifetime of the sensors by minimizing the communication overhead and
data processing cost. Our performance evaluation shows that by using a distributed and incremental approach we are able to
reduce the number of message transfers within the WSN by up to a factor of 5 when compared to a straightforward centralized
Advances in Spatial and Temporal Databases - 12th International Symposium, SSTD 2011, Minneapolis, MN, USA, August 24-26, 2011, Proceedings; 01/2011
[Show abstract][Hide abstract] ABSTRACT: Data measured in wireless sensor networks are inherently imprecise. Aggregate queries are often used to analyze the collected data in order to alleviate the impact of such imprecision. In this paper we will deal with the imprecision in the measured values explicitly by employing a probabilistic approach and we focus on one particular type of aggregate query, namely the SUM query.
SSDBM 2012, Crete, Greece; 08/2012
Note: This list is based on the publications in our database and might not be exhaustive.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.