11 research outputs found
Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data
The k-Nearest Neighbor (kNN) classification approach is conceptually simple -
yet widely applied since it often performs well in practical applications.
However, using a global constant k does not always provide an optimal solution,
e.g., for datasets with an irregular density distribution of data points. This
paper proposes an adaptive kNN classifier where k is chosen dynamically for
each instance (point) to be classified, such that the expected accuracy of
classification is maximized. We define the expected accuracy as the accuracy of
a set of structurally similar observations. An arbitrary similarity function
can be used to find these observations. We introduce and evaluate different
similarity functions. For the evaluation, we use five different classification
tasks based on geo-spatial data. Each classification task consists of (tens of)
thousands of items. We demonstrate, that the presented expected accuracy
measures can be a good estimator for kNN performance, and the proposed adaptive
kNN classifier outperforms common kNN and previously introduced adaptive kNN
algorithms. Also, we show that the range of considered k can be significantly
reduced to speed up the algorithm without negative influence on classification
accuracy
Top-k queries over digital traces
Recent advances in social and mobile technology have enabled an abundance of
digital traces (in the form of mobile check-ins, association of mobile devices
to specific WiFi hotspots, etc.) revealing the physical presence history of
diverse sets of entities (e.g., humans, devices, and vehicles). One challenging
yet important task is to identify k entities that are most closely associated
with a given query entity based on their digital traces. We propose a suite of
indexing techniques and algorithms to enable fast query processing for this
problem at scale. We first define a generic family of functions measuring the
association between entities, and then propose algorithms to transform digital
traces into a lower-dimensional space for more efficient computation. We
subsequently design a hierarchical indexing structure to organize entities in a
way that closely associated entities tend to appear together. We then develop
algorithms to process top-k queries utilizing the index. We theoretically
analyze the pruning effectiveness of the proposed methods based on a mobility
model which we propose and validate in real life situations. Finally, we
conduct extensive experiments on both synthetic and real datasets at scale,
evaluating the performance of our techniques both analytically and
experimentally, confirming the effectiveness and superiority of our approach
over other applicable approaches across a variety of parameter settings and
datasets.Comment: Accepted by SIGMOD2019. Proceedings of the 2019 International
Conference on Management of Dat
Scalable Algorithms for Nearest-Neighbor Joins on Big Trajectory Data (Extended abstract)
Abstract-Trajectory data are prevalent in systems that monitor the locations of moving objects. In a location-based service, for instance, the positions of vehicles are continuously monitored through GPS; the trajectory of each vehicle describes its movement history. We study joins on two sets of trajectories, generated by two sets M and R of moving objects. For each entity in M , a join returns its k nearest neighbors from R. We examine how this query can be evaluated in cloud environments. This problem is not trivial, due to the complexity of the trajectory, and the fact that both the spatial and temporal dimensions of the data have to be handled. To facilitate this operation, we propose a parallel solution framework based on MapReduce. We also develop a novel bounding technique, which enables trajectories to be pruned in parallel. Our approach can be used to parallelize existing single-machine trajectory join algorithms. To evaluate the efficiency and the scalability of our solutions, we have performed extensive experiments on a real dataset
ST-Hadoop: A MapReduce Framework for Big Spatio-temporal Data Management
University of Minnesota Ph.D. dissertation.May 2019. Major: Computer Science. Advisor: Mohamed Mokbel. 1 computer file (PDF); x, 123 pages.Apache Hadoop, employing the MapReduce programming paradigm, that has been widely accepted as the standard framework for analyzing big data in distributed environments. Unfortunately, this rich framework was not genuinely exploited towards processing large scale spatio-temporal data, especially with the emergence and popularity of applications that create them in large-scale. The huge volumes of spatio-temporal data come from applications, like Taxi fleet in urban computing, Asteroids in astronomy research studies, animal movements in habitat studies, neuron analysis in neuroscience research studies, and contents of social networks (e.g., Twitter or Facebook). Managing space and time are two fundamental characteristics that raised the demand for processing spatio-temporal data created by these applications. Besides the massive size of data, the complexity of shapes and formats associated with these data raised many challenges in managing spatio-temporal data. The goal of the dissertation is centered on establishing a full-fledged big spatio-temporal data management system that serves the need for a wide range of spatio-temporal applications. This involves indexing, querying, and analyzing spatio-temporal data. We propose ST-Hadoop; the first full-fledged open-source system with native support for big spatio-temporal data, available to download http://st-hadoop.cs.umn.edu/. ST- Hadoop injects spatio-temporal data awareness inside the highly popular Hadoop system that is considered state-of-the-art for off-line analysis of big data systems. Considering a distributed environment, we focus on the following: (1) indexing spatio-temporal data and (2) Supporting various fundamental spatio-temporal operations, such as range, kNN, and join (3) Supporting indexing and querying trajectories, which is considered as a special class of spatio-temporal data that require special handling. Throughout this dissertation, we will touch base on the background and related work, motivate for the proposed system, and highlight our contributions