SeTraStream: Semantic-Aware Trajectory Construction over Streaming Movement Data

Location data generated from GPS equipped moving objects are typically collected as streams of spatio-temporal (x,y,t) points that when put together form corresponding {\em trajectories}. Most existing studies focus on building ad-hoc querying, analysis, as well as data mining techniques on formed trajectories. As a prior step, trajectory construction is evidently necessary for mobility data processing and understanding -- including tasks like trajectory data cleaning, compression, and segmentation to identify semantic trajectory episodes like stops (e.g. while sitting and standing) and moves (while jogging, walking, driving etc). However, such methods in the current literature, are typically based on offline procedures, which is not sufficient for real life trajectory applications that rely on timely delivery of computed trajectories to serve real time query answers. Filling this gap, our paper proposes a platform, namely SeTraStream, for real-time semantic trajectory construction. Our online framework is capable of providing real-life trajectory data {\em cleaning}, {\em compression}, {\em segmentation} over streaming movement data

Another Outlier Bites the Dust: Computing Meaningful Aggregates in Sensor Networks

Abstract — Recent work has demonstrated that readings pro-vided by commodity sensor nodes are often of poor quality. In order to provide a valuable sensory infrastructure for monitoring applications, we first need to devise techniques that can withstand “dirty ” and unreliable data during query processing. In this paper we present a novel aggregation framework that detects suspicious measurements by outlier nodes and refrains from incorporating such measurements in the computed aggregate values. We consider different definitions of an outlier node, based on the notion of a user-specified minimum support, and discuss techniques for properly routing messages in the network in order to reduce the bandwidth consumption and the energy drain during the query evaluation. In our experiments using real and synthetic traces we demonstrate that: (i) a straightfor-ward evaluation of a user aggregate query leads to practically meaningless results due to the existence of outliers; (ii) our techniques can detect and eliminate spurious readings without any application specific knowledge of what constitutes normal behavior; (iii) the identification of outliers, when performed inside the network, significantly reduces bandwidth and energy drain compared to alternative methods that centrally collect and analyze all sensory data; and (iv) we can significantly reduce the cost of the aggregation process by utilizing simple statistics on outlier nodes and reorganizing accordingly the collection tree. I

ABSTRACT Robust Management of Outliers in Sensor Network Aggregate Queries

Sensor networks are increasingly applied for monitoring diverse environments and applications. Due to their unsupervised nature of operation and inexpensive hardware used, sensor nodes may furnish readings of rather poor quality. We thus need to devise techniques that can withstand “dirty ” data during query processing. In this paper we introduce a robust aggregation framework that can detect and isolate spurious measurements from computed aggregate values. Such readings are not injected in the reported aggregate, in order not to obscure the outcome, but are still maintained and returned to the user/application, which may investigate them further and take appropriate decisions. In addition, our framework provides a form of positive feedback to the user by enhancing the result with a set of nodes that contain the most characteristic values out of those included in the aggregation process. We perform an extensive experimental evaluation of our framework using real traces of sensory data and demonstrate its utility to the monitoring of applications