3,213 research outputs found

    Incremental non-dominated sorting with O(N) insertion for the two-dimensional case.

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    Abstract-We propose a new algorithm for incremental nondominated sorting of two-dimensional points. The data structure which stores non-dominating layers is based on a tree of Cartesian trees. If there are N points in M layers, the running time for of an insertion is O(M (1 + log(N/M )) + log M log(N/ log M )), which is O(N ) in the worst case. This algorithm can be a basic building block for efficient implementations of steady-state multiobjective algorithms such as NSGA-II

    Fast Algorithms for the Computation of Ranklets

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    Ranklets are orientation selective rank features with applications to tracking, face detection, texture and medical imaging. We introduce efficient algorithms that reduce their computational complexity from O(N logN) to O(!N + k), where N is the area of the filter. Timing tests show a speedup of one order of magnitude for typical usage, which should make Ranklets attractive for real-time applications

    StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge

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    Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promises low-latency pattern detection on event streams. However, CEP systems need to be extended with Machine Learning (ML) capabilities such as online training and inference in order to be able to detect fuzzy patterns (e.g., outliers) and to improve pattern recognition accuracy during runtime using incremental model training. In this paper, we propose a distributed CEP system denoted as StreamLearner for ML-enabled complex event detection. The proposed programming model and data-parallel system architecture enable a wide range of real-world applications and allow for dynamically scaling up and out system resources for low-latency, high-throughput event processing. We show that the DEBS Grand Challenge 2017 case study (i.e., anomaly detection in smart factories) integrates seamlessly into the StreamLearner API. Our experiments verify scalability and high event throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS '17), 298-30

    From Proximity to Utility: A Voronoi Partition of Pareto Optima

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    We present an extension of Voronoi diagrams where when considering which site a client is going to use, in addition to the site distances, other site attributes are also considered (for example, prices or weights). A cell in this diagram is then the locus of all clients that consider the same set of sites to be relevant. In particular, the precise site a client might use from this candidate set depends on parameters that might change between usages, and the candidate set lists all of the relevant sites. The resulting diagram is significantly more expressive than Voronoi diagrams, but naturally has the drawback that its complexity, even in the plane, might be quite high. Nevertheless, we show that if the attributes of the sites are drawn from the same distribution (note that the locations are fixed), then the expected complexity of the candidate diagram is near linear. To this end, we derive several new technical results, which are of independent interest. In particular, we provide a high-probability, asymptotically optimal bound on the number of Pareto optima points in a point set uniformly sampled from the dd-dimensional hypercube. To do so we revisit the classical backward analysis technique, both simplifying and improving relevant results in order to achieve the high-probability bounds

    Answering Spatial Multiple-Set Intersection Queries Using 2-3 Cuckoo Hash-Filters

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    We show how to answer spatial multiple-set intersection queries in O(n(log w)/w + kt) expected time, where n is the total size of the t sets involved in the query, w is the number of bits in a memory word, k is the output size, and c is any fixed constant. This improves the asymptotic performance over previous solutions and is based on an interesting data structure, known as 2-3 cuckoo hash-filters. Our results apply in the word-RAM model (or practical RAM model), which allows for constant-time bit-parallel operations, such as bitwise AND, OR, NOT, and MSB (most-significant 1-bit), as exist in modern CPUs and GPUs. Our solutions apply to any multiple-set intersection queries in spatial data sets that can be reduced to one-dimensional range queries, such as spatial join queries for one-dimensional points or sets of points stored along space-filling curves, which are used in GIS applications.Comment: Full version of paper from 2017 ACM SIGSPATIAL International Conference on Advances in Geographic Information System

    Efficient Maxima-Finding Algorithms for Random Planar Samples

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    this paper a simple classification of several known algorithms for finding the maxima, together with several new algorithms; among these are two efficient algorithms---one with expected complexity n +O( # nlogn) when the point samples are issued from some planar regions, and another more efficient than existing one
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