Many research areas depend on group anomaly detection. The use of detection can maintain and provide security privacy to the data involved. This attempts solve deficiency existing literature in outlier thus a novel hybrid framework identify from sequence is proposed this paper. It proposes two approaches for efficiently solving problem: i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" ...

different methods arising from scientific investigation for removing noise from chaotic signals have been introduced and one of their best is local projection approach. the local projection approach projects the chaotic data in the neighborhood onto the hyper-plane. selection of neighborhood radius has a direct impact on its performance. a noticeable feature associated with chaotic trajectories...

The new positioning and information capture technologies are able to treat data related to moving objects taking place in targeted phenomena. This gave birth to a new data source type called trajectory data (TD) which handle information related to moving objects. Trajectory Data must be integrated in a new data warehouse type called trajectory data warehouse (TDW) that is essential to model and...

With the continued advancements in location-based services involved infrastructures, large amount of time-based location data are quickly accumulated. Distributed processing techniques on such large trajectory data sets are urgently needed. We propose TRUSTER: a distributed trajectory data processing system on clusters. TRUSTER employs a distributed indexing method on large scale trajectory dat...

Spatio-temporal patterns extracted from historical trajectories of moving objects reveal important knowledge about movement behavior for high quality LBS services. Existing approaches transform trajectories into sequences of location symbols and derive frequent subsequences by applying conventional sequential pattern mining algorithms. However, spatio-temporal correlations may be lost due to th...

In the regime of “Big Data”, data compression techniques take crucial part in preparation phase of data analysis. It is challenging because statistical properties and other characteristics need to be preserved while the size of data need to be reduced. In particular, to compress trajectory data, movement status (such as position, direction, and speed etc.) need to be retained. In this paper, we...

In this paper we investigate some issues and solutions related to the design of a Data Warehouse (DW), storing several aggregate measures about trajectories of moving objects. First we discuss the loading phase of our DW which has to deal with overwhelming streams of trajectory observations, possibly produced at different rates, and arriving in an unpredictable and unbounded way. Then, we focus...

We demonstrate that all available inclusive pp and p̄p single diffraction data from ISR to Tevatron require an effective Pomeron trajectory whose intercept, α(0), changes with energy; it decreases from 1.10 at low energy to about 1.03 at the SPS and perhaps smaller at the Tevatron. This result, when combined with a relatively s–independent trajectory, α(t) ∼ 0.92 − 0.95 in the range, 1.0 < |t| <...

With the rapid increase in the use of inexpensive, location-aware sensors in a variety of new applications, large amounts of time-sequenced location data will soon be accumulated. Efficient indexing techniques for managing these large volumes of trajectory data sets are urgently needed. The key requirements for a good trajectory indexing technique is that it must support both searches and inser...

Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road networ...