Francesco Lettich

GPU-Based Computing of Repeated Range Queries over Moving Objects

In this paper we investigate the use of GPUs to solve a data-intensive problem that involves huge amounts of moving objects. The scenario which we focus on regards objects that continuously move in a 2D space, where a large percentage of them also issues range queries. The processing of these queries entails a large quantity of objects falling into the range queries to be returned. In order to solve this problem by maintaining a suitable throughput, we partition the time into ticks, and defer the parallel processing of all the objects events (location updates and range queries) occurring in a given tick to the next tick, thus slightly delaying the overall computation. We process in parallel all the events of each tick by adopting an hybrid approach, based on the combined use of CPU and GPU, and show the suitability of the method by discussing performance results. The exploitation of a GPU allow us to achieve a speedup of more than 20× on several datasets with respect to the best sequential algorithm solving the same problem. More importantly, we show that the adoption of new bitmap-based intermediate data structure we propose to avoid memory access contention entails a 10× speedup with respect to naive GPU based solutions.

Detecting avoidance behaviors between moving object trajectories

Abstract Several algorithms have been proposed in the last few years for mining different mobility patterns from trajectories, such as flocks, chasing, meeting, and convergence. An interesting behavior that has not been much explored in trajectory pattern mining is avoidance. In this paper we define the avoidance behavior between moving object trajectories, providing a set of theoretical definitions to precisely describe various kinds of avoidance, and propose an effective algorithm for detecting avoidances. The proposed method is quantitatively evaluated on a real-world dataset, and correctly detects with high precision the quasi totality of the trajectory pairs that exhibit avoidance behaviors (F-measure up to 95%).

Processing streams of spatial k-NN queries and position updates on manycore GPUs

The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. In this paper we focus on a specific data-intensive problem concerning the repeated processing of huge amounts of k nearest neighbours (k-NN) queries over massive sets of moving objects, where the spatial extents of queries and the position of objects are continuously modified over time. In particular, we propose a novel hybrid CPU/GPU pipeline that significantly accelerate query processing thanks to a combination of ad-hoc data structures and non-trivial memory access patterns. To the best of our knowledge this is the first work that exploits GPUs to efficiently solve repeated k-NN queries over massive sets of continuously moving objects, even characterized by highly skewed spatial distributions. In comparison with state-of-the-art sequential CPU-based implementations, our method highlights significant speedups in the order of 10x-20x, depending on the datasets, even when considering cheap GPUs.

TPRED: a Spatio-Temporal Location Predictor Framework

The vast diffusion of devices equipped with a GPS receiver has brought the possibility of collecting data related to massive amounts of moving objects on a scale never seen before. During the latest years, such diffusion instigated the development of many different techniques to deal with location prediction problems. Existing works mainly aim at predicting the next location of moving objects by focusing on information in the spatial domain. In this paper we want to take into account information in the temporal domain as well, both to improve the reliability of predictions and to answer not only where a moving object is going to move, but also when an object is expected to leave its current location. To this end we propose Tpred, a framework based on probabilistic suffix trees which tries to capture typical movement patterns of moving objects, and computes reliable predictions accordingly, by exploiting information both in the spatial and temporal domains. In order to prove the validity of our contribution we conduct an extensive set of experimental evaluations, based on real-world datasets and different performance metrics, where we show the efficiency and effectiveness of our proposal.

Multicore/Manycore Parallel Traversal of Large Forests of Regression Trees

Machine-learnt models based on additive ensembles of binary regression trees are currently considered one of the best solutions to address complex classification, regression, and ranking tasks. To evaluate these complex models over a continuous stream of data items with high throughput requirements, we need to optimize, and possibly parallelize, the traversal of thousands of trees, each including hundreds of nodes.Document ranking in Web Search is a typical example of this challenging scenario, where complex tree-based models are used to score query-document pairs and finally rank lists of document results for each incoming query (a.k.a. Learning-to-Rank). In this extended abstract, we briefly discuss some preliminary results concerning the parallelization strategies for QUICKSCORER – indeed the state-of-art scoring algorithm that exploits ensembles of decision trees – by using multicore CPUs (with SIMD coprocessors) and manycore GPUs. We show that QUICKSCORER, which transforms the traversal of thousands of decision trees in a linear access to array data structures, can be parallelized very effectively, by achieving very interesting speedups.

An Accurate Tax Fraud Classifier with Feature Selection based on Complex Network Node Centrality Measure.

Fiscal evasion represents a very serious issue in many developing countries. In this context, tax fraud detection constitutes a challenging problem, since fraudsters change frequently their behaviors to circumvent existing laws and devise new kinds of frauds. Detecting such changes proves to be challenging, since traditional classifiers fail to select features that exhibit frequent variations. In this paper we provide two contributions that try to tackle effectively the tax fraud detection problem: first, we introduce a novel feature selection algorithm, based on complex network techniques, that is able to capture key fraud indicators – over time, this kind of indicators turn out to be more stable than new fraud indicators. Secondly, we propose a classifier that leverages the aforementioned algorithm to accurately detect tax frauds. In order to prove the validity of our contributions we provide an experimental evaluation, where we use real-world datasets, obtained from the State Treasury Office of Ceará (SEFAZ-CE), Brazil, to show how our method is able to outperform, in terms of F1 scores achieved, the state-of-the-art available in the literature.

Manycore GPU processing of repeated range queries over streams of moving objects observations

The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. Parallelism enables such applications to face this data‐intensive challenge and allows the devised systems to feature low latency and high scalability. In this paper, we focus on a specific data‐intensive problem concerning the repeated processing of huge amounts of range queries over massive sets of moving objects, where the spatial extent of queries and objects is continuously modified over time. To tackle this problem and significantly accelerate query processing, we devise a hybrid CPU/GPU pipeline that compresses data output and saves query processing work. The devised system relies on an ad‐hoc spatial index leading to a problem decomposition that results in a set of independent data‐parallel tasks. The index is based on a point‐region quadtree space decomposition and allows to tackle effectively a broad range of spatial object distributions, even those very skewed. Also, to deal with the architectural peculiarities and limitations of the GPUs, we adopt non‐trivial GPU data structures that avoid the need of locked memory accesses while favouring coalesced memory accesses, thus enhancing the overall memory throughput. To the best of our knowledge, this is the first work that exploits GPUs to efficiently solve repeated range queries over massive sets of continuously moving objects, possibly characterized by highly skewed spatial distributions. In comparison with state‐of‐the‐art CPU‐based implementations, our method highlights significant speedups in the order of 10 − 20×, depending on the dataset. Copyright

Mining condensed spatial co-location patterns

The discovery of co-location patterns among spatial events is an important task in spatial data mining. We introduce a new kind of spatial co-location patterns, named condensed spatial co-location patterns, that can be considered as a lossy compressed representation of all the co-location patterns. Each condensed pattern is the representative, and a superset, of a group of spatial co-location patterns in the full set of patterns such that the difference between the interestingness measure of the representative and the measures of the patterns belonging to the associated group are negligible. Our preliminary experiments show that condensed spatial co-location patterns are less sensitive to parameter changes and more robust in presence of missing data than closed spatial co-location patterns.