Detecting the Direction of Motion in a Binary Sensor Network

We examine the problem of detecting the direction of motion in a binary sensor network; in such a network each sensor’s value is supplied reliably in a single bit of information: whether the moving object is approaching towards or moving away from the sensor. We demonstrate that the geometric properties of the network itself can be exploited for the detection of movement direction, from a single instance of sensor reading only. Moreover the estimation is performed in a distributed processing fashion, with only a minimal data collection at situation-dependent leading sensors and features a low computational burden on each sensor. In addition, different detection instances drain the resources of different groups of sensors, of a small size compared to the size of the whole network. Our experiments demonstrate high accuracy that increases with sensor density and/or sensing range, while the responsiveness of the detection model is practically instantaneous.published_or_final_versio

Forward Scan based Plane Sweep Algorithm for Parallel Interval Joins

The interval join is a basic operation that finds application in temporal, spatial, and uncertain databases. Although a number of centralized and distributed algorithms have been proposed for the efficient evaluation of interval joins, classic plane sweep approaches have not been considered at their full potential. A recent piece of related work proposes an optimized approach based on plane sweep (PS) for modern hardware, showing that it greatly outperforms previous work. However, this approach depends on the development of a complex data structure and its parallelization has not been adequately studied. In this paper, we explore the applicability of a largely ignored forward scan (FS) based plane sweep algorithm, which is extremely simple to implement. We propose two optimizations of FS that greatly reduce its cost, making it competitive to the state-of-the-art single-threaded PS algorithm while achieving a lower memory footprint. In addition, we show the drawbacks of a previously proposed hash-based partitioning approach for parallel join processing and suggest a domain-based partitioning approach that does not produce duplicate results. Within our approach we propose a novel breakdown of the partition join jobs into a small number of independent mini-join jobs with varying cost and manage to avoid redundant comparisons. Finally, we show how these mini-joins can be scheduled in multiple CPU cores and propose an adaptive domain partitioning, aiming at load balancing. We include an experimental study that demonstrates the efficiency of our optimized FS and the scalability of our parallelization framework.published_or_final_versio

Frequent-pattern based iterative projected clustering

Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately. © 2003 IEEE.published_or_final_versio

Frequent-pattern based iterative projected clustering

Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately. © 2003 IEEE.published_or_final_versio

K−/K+K^-/K^+ multiplicity ratio for kaons produced in DIS with a large fraction of the virtual photon energy

For the first time, the $K^-/K^+$ multiplicity ratio is measured in deep-inelastic scattering for kaons carrying a large fraction $z$ of the virtual-photon energy. The data were obtained by the COMPASS collaboration using a 160 GeV muon beam and an isoscalar $^6$LiD target. The regime of deep-inelastic scattering is ensured by requiring $Q^2>1$ (GeV/$c)^2$ for the photon virtuality and $W>5$ GeV/$c^2$ for the invariant mass of the produced hadronic system. The Bjorken scaling variable range is $0.010.75$. For very large values of $z$, {\it i.e.} $z>0.8$, the results contradict expectations obtained using the formalism of (next-to-)leading order perturbative quantum chromodynamics. Our studies suggest that, within this formalism, an additional correction may be required to take into account the phase space available for hadronisation.Peer Reviewe

First look at average-case complexity for planar maximum-likelihood detection

In this paper, an efficient exact maximum-likelihood (ML) detection scheme is presented for a multiple-input singleoutput (MI SO) system with real signal constellations. The proposed technique has a geometrical interpretation of exploring the points jointly "close" in all coordinate axes around the decoding hyperplane and is therefore dubbed planar detection. The fact that the lattice points which are close in all coordinate axes are much less, leads to dramatic reduction in detection complexity. Making a few approximations, this paper derives the average-case complexity exponent, ec, for planar detection analytically in a closed form. Numerical results show that for an (n, 1) 1 system, although the expected complexity is still exponential, complexity reduction of 2 exponents, i.e., from ec to ec - 2, is realized and such advantage is promised irrespective of the size of the signal constellations and the received signal-to-noise ratio (SNR). © 2005 IEEE.published_or_final_versio

Clustering in Geo-Social Networks

The rapid growth of Geo-Social Networks (GeoSNs) provides a new and rich form of data. Users of GeoSNs can capture their geographic locations and share them with other users via an operation named checkin. Thus, GeoSNs can track the connections (and the time of these connections) of geographic data to their users. In addition, the users are organized in a social network, which can be extended to a heterogeneous network if the connections to places via checkins are also considered. The goal of this paper is to analyze the opportunities in clustering this rich form of data. We first present a model for clustering geographic locations, based on GeoSN data. Then, we discuss how this model can be extended to consider temporal information from checkins. Finally, we study how the accuracy of community detection approaches can be improved by taking into account the checkins of users in a GeoSN.published_or_final_versio

Improving microblog retrieval from exterior corpus by automatically constructing a microblogging corpus

A large-scale training corpus consisting of microblogs belonging to a desired category is important for highaccuracy microblog retrieval. Obtaining such a large-scale microblgging corpus manually is very time and laborconsuming. Therefore, some models for the automatic retrieval of microblogs from an exterior corpus have been proposed. However, these approaches may fail in considering microblog-specific features. To alleviate this issue, we propose a methodology that constructs a simulated microblogging corpus rather than directly building a model from the exterior corpus. The performance of our model is better since the microblog-special knowledge of the microblogging corpus is used in the end by the retrieval model. Experimental results on real-world microblogs demonstrate the superiority of our technique compared to the previous approaches.postprin

Textually Relevant Spatial Skylines

postprin

Diverse and proportional size-1 object summaries for keyword search

The abundance and ubiquity of graphs (e.g., Online Social Networks such as Google+ and Facebook; bibliographic graphs such as DBLP) necessitates the effective and efficient search over them. Given a set of keywords that can identify a Data Subject (DS), a recently proposed relational keyword search paradigm produces, as a query result, a set of Object Summaries (OSs). An OS is a tree structure rooted at the DS node (i.e., a tuple containing the keywords) with surrounding nodes that summarize all data held on the graph about the DS. OS snippets, denoted as size-l OSs, have also been investigated. Size-l OSs are partial OSs containing l nodes such that the summation of their importance scores results in the maximum possible total score. However, the set of nodes that maximize the total importance score may result in an uninformative size-l OSs, as very important nodes may be repeated in it, dominating other representative information. In view of this limitation, in this paper we investigate the effective and efficient generation of two novel types of OS snippets, i.e. diverse and proportional size-l OSs, denoted as DSize-l and PSize-l OSs. Namely, apart from the importance of each node, we also consider its frequency in the OS and its repetitions in the snippets. We conduct an extensive evaluation on two real graphs (DBLP and Google+). We verify effectiveness by collecting user feedback, e.g. by asking DBLP authors (i.e. the DSs themselves) to evaluate our results. In addition, we verify the efficiency of our algorithms and evaluate the quality of the snippets that they produce.postprin