Mario A. Nascimento

A compact and efficient image retrieval approach based on border/interior pixel classification

This paper presents \bic (Border/Interior pixel Classification), a compact and efficient CBIR approach suitable for broad image domains. It has three main components: (1) a simple and powerful image analysis algorithm that classifies image pixels as either border or interior, (2) a new logarithmic distance (dLog) for comparing histograms, and (3) a compact representation for the visual features extracted from images. Experimental results show that the BIC approach is consistently more compact, more efficient and more effective than state-of-the-art CBIR approaches based on sophisticated image analysis algorithms and complex distance functions. It was also observed that the dLog distance function has two main advantages over vectorial distances (e.g., L1): (1) it is able to increase substantially the effectiveness of (several) histogram-based CBIR approaches and, at the same time, (2) it reduces by 50% the space requirement to represent a histogram.

SpADe: On Shape-based Pattern Detection in Streaming Time Series

Monitoring predefined patterns in streaming time series is useful to applications such as trend-related analysis, sensor networks and video surveillance. Most current studies on such monitoring employ Euclidean distance to calculate the similarities between given query patterns and subsequences of streaming time series. Euclidean distance has been shown to be ineffective in measuring distances of time series in which shifting and scaling usually exist. Consequently, warping distances such as dynamic time warping (DTW), longest common subsequence (LCSS), have been proposed to handle warps in temporal dimension. However, they are inadequate in handling shifting and scaling in amplitude dimension. Moreover, they have been designed mainly for full sequence matching, whereas in online monitoring applications, we typically have no knowledge on the positions and lengths of possible matching subsequences. In this paper, we first discuss the weaknesses of existing warping distances on detecting patterns from streaming time series. We then propose a novel warping distance, which we name Spatial Assembling Distance (SpADe), that is able to handle shifting and scaling in both temporal and amplitude dimensions. We further propose an efficient approach for continuous pattern detection using SpADe, that is fundamental for subsequence matching on streaming data. Finally, our experimental results show that SpADe is effective and efficient for continuous pattern detection in streaming time series.

ST 2 B-tree: a self-tunable spatio-temporal b + -tree index for moving objects

In a moving objects database (MOD) the dataset and the workload change frequently. As the locations of objects change in space and time, the data distribution also changes and the answer for a same query over the same region may vary widely over time. As a result, traditional static indexes are not able to perform well and it is critical to develop self-tuning indexes that can be reconfigured automatically based on the state of the system. Towards this goal we propose the ST2B-tree, a Self-Tunable Spatio-Temporal B+-Tree index for MODs, which is amenable to tuning. Frequent updates to its subtrees allows rebuilding (tuning) a subtree using a different set of reference points and different grid size without significant overhead. We also present an online tuning framework for the ST2B-tree, where the tuning is conducted online and automatically without human intervention, also not interfering with regular functions of the MOD. Our extensive experiments show that the self-tuning process minimizes the effectiveness degradation of the index caused by workload changes at the cost of virtually no overhead.

Aggregation convergecast scheduling in wireless sensor networks

We consider the problem of scheduling in wireless sensor networks for the purposes of aggregation convergecast. We observe that existing schemes adopt essentially a two phase approach, consisting of, first, a tree construction and, second, a scheduling phase. Following a similar approach, we propose two new improvements, one to each of the two phases. Starting with a new lower bound on the schedule length, we make use of it in the tree construction phase. The tree construction phase consists of solutions to instances of bipartite graph semi-matchings. The scheduling phase is a weight-based priority scheme that obeys dependency (tree) and interference constraints. Our extensive experiments show that, overall, our proposed solution not only outperforms all previously proposed solutions in terms of schedule length, but it also significantly extends the network’s lifetime.

A framework for spatio-temporal query processing over wireless sensor networks

Wireless sensor networks consist of nodes with the ability to measure, store, and process data, as well as to communicate wirelessly with nodes located in their wireless range. Users can issue queries over the network, e.g., retrieve information from nodes within a specified region, in applications such as environmental monitoring. Since the sensors have typically only a limited power supply, energy-efficient processing of the queries over the network is an important issue. In this paper, we introduce a general framework for distributed processing of spatio-temporal queries in a sensor network that has two main phases: (1) routing the query to the spatial area specified in the query; (2) collecting and processing the information from the nodes relevant to the query. Within this framework, different algorithms can be designed independently for each of the two phases. We also propose novel algorithms for this framework, one for the first phase and two for the second phase. In an extensive experimental evaluation we study the performance of these algorithms in terms of energy consumption, under varying conditions. The results allow us to recommend the most energy efficient solution, given a network and a spatiotemporal query.

PIST: An Efficient and Practical Indexing Technique for Historical Spatio-Temporal Point Data

Despite pressing need, current relational database management systems (RDBMS) support for spatio-temporal data is limited and inadequate, and most existing spatio-temporal indices cannot be readily integrated into existing RDBMSs. This paper proposes a practical index for spatio-temporal (PIST) data, an indexing technique, rather than a new indexing structure, for historical spatio-temporal data points that can be fully integrated within existing RDBMSs. PIST separates the spatial and temporal components of the data. For the spatial component, we develop a formal cost model and a partitioning strategy that leads to an optimal space partitioning for uniformly distributed data and an efficient heuristic partitioning for arbitrary data distributions. For the temporal component of the data a B + -tree is used. We show that this layer’s performance can be maximized if an optimal maximal temporal range is enforced, and we present a procedure to determine such an optimal value. Being fully mapped onto a RDBMS, desirable and important properties, such as concurrency control, are immediately inherited by PIST. Using ORACLE as our implementation platform we perform extensive experiments with both real and synthetic datasets comparing its performance against other RDBMS-based options, as well as the MV3R-tree. PIST outperforms the former by at least one order of magnitude, and is competitive or better with respect to the latter, with the unarguable advantage that it can readily used on top of virtually any existing RDBMS.

Generating spatiotemporal datasets on the WWW

Efficient storage, indexing and retrieval of time-evolving spatial data are some of the tasks that a Spatiotemporal Database Management System (STDBMS) must support. Aiming at designers of indexing methods and access structures, in this article we review the GSTD algorithm for generating spatiotemporal datasets according to several user-defined parameters, and introduce a WWW-based environment for generating and visualizing such datasets. The GSTD interface is available at two main sites: http://www.cti.gr/RD3/GSTD/ and http://www.cs.ualberta.ca/~mn/GSTD/.

Indexing valid time databases via B/sup +/-trees

We present an approach, named MAP21, which uses standard B/sup +/-trees to provide efficient indexing of valid time ranges. The MAP21 approach is based on mapping one dimensional ranges to one dimensional points where the lexicographical order among the ranges is preserved. The proposed approach may employ more than one tree, each indexing a disjoint subset of the indexed data. When compared to the Time Index and the B/sup +/-tree we show that MAP21s performance is comparable to or better than those, depending on the type of query. In terms of storage, MAP21s structure was less than 10 percent larger than the B/sup +/-trees and much smaller than the Time Indexs. The main contribution of this paper though, is to show that standard B/sup +/-trees, available in virtually any DBMS, can be used to provide an efficient temporal index.

An Analysis of Spatio-Temporal Query Processing in Sensor Networks

Sensor networks are an emerging technology that provide new means to monitor and understand various phenomena. Nodes in a sensor network typically have a limited power supply, thus energy-efficient processing of the queries over the network is a critical issue. We propose analytical models to evaluate the performance of three methods for processing historical spatio-temporal queries in sensor networks. The models allow us to better understand the tradeoffs of the investigated methods, as well as to recommend the most energy efficient one at query time.

Exact Top-K Queries in Wireless Sensor Networks

In this paper, we consider the exact top-k query problem in wireless sensor networks, i.e., where one seeks to find the k highest reported values as well as the complete set of nodes that reported them. Our primary contribution in this context is EXTOK, a provably correct and topology-independent new filtering-based algorithm for processing exact top-k queries. As a secondary contribution we confirm a previous result of ours by showing that the efficiency of top-k query processing algorithms, including EXTOK, can be further improved by simply choosing a proper underlying logical tree topology. We examine EXTOKs performance with respect to a number of parameters and different logical tree topologies while using both synthetic and real data sets. Our simulation reveal that EXTOK consistently outperforms the current state-of-the-art algorithm by a very significant margin and regardless of the underlying logical tree topology.