Continuous Nearest Neighbor Queries over Sliding Windows

Abstract—This paper studies continuous monitoring of nearest neighbor (NN) queries over sliding window streams. According to this model, data points continuously stream in the system, and they are considered valid only while they belong to a sliding window that contains 1) the W most recent arrivals (count-based) or 2) the arrivals within a fixed interval W covering the most recent time stamps (time-based). The task of the query processor is to constantly maintain the result of long-running NN queries among the valid data. We present two processing techniques that apply to both count-based and time-based windows. The first one adapts conceptual partitioning, the best existing method for continuous NN monitoring over update streams, to the sliding window model. The second technique reduces the problem to skyline maintenance in the distance-time space and precomputes the future changes in the NN set. We analyze the performance of both algorithms and extend them to variations of NN search. Finally, we compare their efficiency through a comprehensive experimental evaluation. The skyline-based algorithm achieves lower CPU cost, at the expense of slightly larger space overhead. Index Terms—Location-dependent and sensitive, spatial databases, query processing, nearest neighbors, data streams, sliding windows.

Cloud Data Analysis Service With Efficient In Large Scale Social Networks

Social network analysis in various methods on basis an amount computation of feature extraction process has to a great extent to separate into constituent parts of social network. The Feature Extraction Process (FEP) suffers from serious computational and communication skews. The data dependency graph of FEPs may be known only at execution time and changes dynamically. It not only makes it hard to evaluate each task’s load, but also leaves some computers underutilized after the convergence of most features in early iterations. In Social network analysis put to practical use to pull structure relating to human an interacting population of various kinds of individuals Social network analysis directs highly effective in variety of scientific domains. The intension of involving straggler-having act to draw closer, SAE, to give assistance to the identification function of serving in the cloud. a important challenge to effective information analysis is the computation and conversation skew (i.E., load imbalance) among desktops prompted through humanity’s team behaviour (e.G., bandwagon influence). Natural load balancing procedures either require gigantic effort to re- balance masses on the nodes, or cannot good cope with stragglers. On this paper, we recommend a general straggler-aware execution method, SAE, to aid the evaluation carrier within the cloud. It presents a novel computational decomposition procedure that causes straggling function extraction tactics into more excellent-grained sub strategies, that are then allotted over clusters of computers for parallel execution

Efficient query processing over uncertain road networks

One of the fundamental problems on spatial road networks has been the shortest traveling time query, with applications such as location-based services (LBS) and trip planning. Algorithms have been made for the shortest time queries in deterministic road networks, in which vertices and edges are known with certainty. Emerging technologies are available and make it easier to acquire information about the traffic. In this paper, we consider uncertain road networks, in which speeds of vehicles are imprecise and probabilistic. We will focus on one important query type, continuous probabilistic shortest traveling time query (CPSTTQ), which retrieves sets of objects that have the smallest traveling time to a moving query point q from point s to point e on road networks with high confidences. We propose effective pruning methods to prune the search space of our CPSTTQ query, and design an efficient query procedure to answer CPSTTQ via an index structure

Exploring historical location data for anonymity preservation in location-based services

We present a new approach for K-anonymity protection in Location-Based Services (LBSs). Specifically, we depersonalize location information by ensuring that each location reported for LBSs is a cloaking area that contains K different footprints--- historical locations of different mobile nodes. Therefore, the exact identity and location of the service requestor remain anonymous from LBS service providers. Existing techniques, on the other hand, compute the cloaking area using current locations of K neighboring hosts of the service requestor. Because of this difference, our approach significantly reduces the cloaking area, which in turn decreases query processing and communication overhead for returning query results to the requesting host. In addition, existing techniques also require frequent location updates from all nodes, regardless of whether or not these nodes are requesting LBSs. Most importantly, our approach is the first practical solution that provides K-anonymity trajectory protection needed to ensure anonymity when the mobile host requests LBSs continuously as it moves. Our solution depersonalizes a user\u27s trajectory (a time-series of the user\u27s locations) based on the historical trajectories of other users

Efficient data structures for model-free data-driven computational mechanics

The data-driven computing paradigm initially introduced by Kirchdoerfer & Ortiz (2016) enables finite element computations in solid mechanics to be performed directly from material data sets, without an explicit material model. From a computational effort point of view, the most challenging task is the projection of admissible states at material points onto their closest states in the material data set. In this study, we compare and develop several possible data structures for solving the nearest-neighbor problem. We show that approximate nearest-neighbor (ANN) algorithms can accelerate material data searches by several orders of magnitude relative to exact searching algorithms. The approximations are suggested by—and adapted to—the structure of the data-driven iterative solver and result in no significant loss of solution accuracy. We assess the performance of the ANN algorithm with respect to material data set size with the aid of a 3D elasticity test case. We show that computations on a single processor with up to one billion material data points are feasible within a few seconds execution time with a speed up of more than 10⁶ with respect to exact k-d trees

Authentication of Moving Top-k Spatial Keyword Queries

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