Vincent Oria

Robust and fast similarity search for moving object trajectories

An important consideration in similarity-based retrieval of moving object trajectories is the definition of a distance function. The existing distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, disturbance signals, and different sampling rates. Cleaning data to eliminate these is not always possible. In this paper, we introduce a novel distance function, Edit Distance on Real sequence (EDR) which is robust against these data imperfections. Analysis and comparison of EDR with other popular distance functions, such as Euclidean distance, Dynamic Time Warping (DTW), Edit distance with Real Penalty (ERP), and Longest Common Subsequences (LCSS), indicate that EDR is more robust than Euclidean distance, DTW and ERP, and it is on average 50% more accurate than LCSS. We also develop three pruning techniques to improve the retrieval efficiency of EDR and show that these techniques can be combined effectively in a search, increasing the pruning power significantly. The experimental results confirm the superior efficiency of the combined methods.

Symbolic representation and retrieval of moving object trajectories

Searching moving object trajectories of video databases has been applied to many fields, such as video data analysis, content-based video retrieval, video scene classification. In this paper, we propose a novel representation of trajectories, called <i>movement pattern strings</i>, which convert the trajectories into symbolic representations. Movement pattern strings encode both the movement direction and the movement distance information of the trajectories. The distances that are computed in a symbolic space are lower bounds of the distances of original trajectory data, which guarantees that no false dismissals will be introduced using movement pattern strings to retrieve trajectories. In order to improve the retrieval efficiency, we define a <i>modified frequency distance</i> for frequency vectors that are obtained from movement pattern strings to reduce the dimensionality and the computation cost. The experimental results show that using movement pattern strings is almost as effective as using raw trajectories. In addition, the cost of retrieving similar trajectories can greatly be reduced when the modified frequency distance is used as a filter

Benchmarking spatial joins a la carte

Spatial joins are join operations that involve spatial data types and operators. Spatial access methods are often used to speed up the computation of spatial joins. This paper addresses the issue of benchmarking spatial join operations. For this purpose, we first present a WWW-based benchmark generator to produce sets of rectangles. Using a Web browser, experimenters can specify the number of rectangles in a sample, as well as the statistical distributions of their sizes, shapes, and locations. Second, using the generator and a well-defined set of statistical models we define several tests to compare the performance of three spatial join algorithms: nested loop, scan-and-index, and synchronized tree traversal. We also added two real-life data sets from the Sequoia 2000 storage benchmark. Our results show that the relative performance of the different techniques mainly depends on the selectivity factor of the join predicate. All of the statistical models and algorithms are available on the Web, which allow...

Indexing in-network trajectory flows

Indexing moving objects (MO) is a hot topic in the field of moving objects databases since many years. An impressive number of access methods have been proposed to optimize the processing of MO-related queries. Several methods have focused on spatio-temporal range queries, which represent the foundation of MO trajectory queries. Surprisingly, only a few of them consider that the objects movements are constrained. This is an important aspect for several reasons ranging from better capturing the relationship between the trajectory and the network space to more accurate trajectory representation with lower storage requirements. In this paper, we propose T-PARINET, an access method to efficiently retrieve the trajectories of objects moving in networks. T-PARINET is designed for continuous indexing of trajectory data flows. The cornerstone of T-PARINET is PARINET, an efficient index for historical trajectory data. The structure of PARINET is based on a combination of graph partitioning and a set of composite B+-tree local indexes. Because the network can be modeled using graphs, the partitioning of the trajectory data makes use of graph partitioning theory and can be tuned for a given query load and a given data distribution in the network space. The tuning process is built on a good quality cost model that is supplied with PARINET. The advantage of having a cost model is twofold; it allows a better integration of the index into the query optimizer of any DBMS, and it permits tuning the index structure for better performance. The tuning process can be performed before the index creation in the case of historical data or online in the case of indexing data flows. In fact, massive online updates can degrade the index quality, which can be measured by the cost model. We propose a specific maintenance process that results into T-PARINET. We study different types of queries and provide an optimized configuration for several scenarios. T-PARINET can easily be integrated into any RDBMS, which is an essential asset particularly for industrial or commercial applications. The experimental evaluation under an off-the-shelf DBMS shows that our method is robust. It also significantly outperforms the reference R-tree-based access methods for in-network trajectory databases.

Dimensional Testing for Multi-step Similarity Search

In data mining applications such as subspace clustering or feature selection, changes to the underlying feature set can require the reconstruction of search indices to support fundamental data mining tasks. For such situations, multi-step search approaches have been proposed that can accommodate changes in the underlying similarity measure without the need to rebuild the index. In this paper, we present a heuristic multi-step search algorithm that utilizes a measure of intrinsic dimension, the generalized expansion dimension (GED), as the basis of its search termination condition. Compared to the current state-of-the-art method, experimental results show that our heuristic approach is able to obtain significant improvements in both the number of candidates and the running time, while losing very little in the accuracy of the query results.

Structuralizing educational videos based on presentation content

This work addresses the challenge of extracting structure in educational and training media based on the type of material that is presented during lectures and training sessions. The narrative structure that arises out of a use of different types of presentation content such as slides, web pages, and white board writings is useful in segmenting an educational video for easy content access and nonlinear browsing of the material presented. Automatically detecting sections of videos as delineated by the use of supplementary teaching/instructional visual aids allows for structuralizing educational video with high level of semantics, and provides a concise means for organizing learning content according to the needs of different users in e-learning scenarios. Experiments on the videos from classrooms show encouraging results with discriminating different narrative sections in the proposed presented-material based video structuralization scheme.

VisualMOQL: the DISIMA visual query language

Multimedia data are now available to a variety of users ranging from naive to sophisticated. To make querying easy, visual query languages have been proposed. Most of these languages have a low expressive power and have their own query processors. Efforts have been made to design query languages with proper semantics to facilitate query optimization and processing in existing database systems. The majority of multimedia database systems are built on top of object or object-relational database systems with the underlying query facilities inherited. The DISIMA system is being built on top of a commercial OODBMS and we have chosen to extend the standard object oriented query language OQL with some multimedia functionalities. The resulting language is called MOQL. This paper presents VisualMOQL, a visual query language implementing the image component of MOQL.

Spatio-temporal compression of trajectories in road networks

With the proliferation of wireless communication devices integrating GPS technology, trajectory datasets are becoming more and more available. The problems concerning the transmission and the storage of such data have become prominent with the continuous increase in volume of these data. A few works in the field of moving object databases deal with spatio-temporal compression. However, these works only consider the case of objects moving freely in the space. In this paper, we tackle the problem of compressing trajectory data in road networks with deterministic error bounds. We analyze the limitations of the existing methods and data models for road network trajectory compression. Then, we propose an extended data model and a network partitioning algorithm into long paths to increase the compression rates for the same error bound. We integrate these proposals with the state-of-the-art Douglas-Peucker compression algorithm to obtain a new technique to compress road network trajectory data with deterministic error bounds. The extensive experimental results confirm the appropriateness of the proposed approach that exhibits compression rates close to the ideal ones with respect to the employed Douglas-Peucker compression algorithm.

Flexible integration of multimedia sub-queries with qualitative preferences

Complex multimedia queries, aiming to retrieve from large databases those objects that best match the query specification, are usually processed by splitting them into a set of m simpler sub-queries, each dealing with only some of the query features. To determine which are the overall best-matching objects, a rule is then needed to integrate the results of such sub-queries, i.e., how to globally rank the m-dimensional vectors of matching degrees, or partial scores, that objects obtain on the m sub-queries. It is a fact that state-of-the-art approaches all adopt as integration rule a scoring function, such as weighted average, that aggregates the m partial scores into an overall (numerical) similarity score, so that objects can be linearly ordered and only the highest scored ones returned to the user. This choice however forces the system to compromise between the different sub-queries and can easily lead to miss relevant results. In this paper we explore the potentialities of a more general approach, based on the use of qualitative preferences, able to define arbitrary partial (rather than only linear) orders on database objects, so that a larger flexibility is gained in shaping what the user is looking for. For the purpose of efficient evaluation, we propose two integration algorithms able to work with any (monotone) partial order (thus also with scoring functions): MPO, which delivers objects one layer of the partial order at a time, and iMPO, which can incrementally return one object at a time, thus also suitable for processing top k queries. Our analysis demonstrates that using qualitative preferences pays off. In particular, using Skyline and Region-prioritized Skyline preferences for queries on a real image database, we show that the results we get have a precision comparable to that obtainable using scoring functions, yet they are obtained much faster, saving up to about 70% database accesses.Complex multimedia queries, aiming to retrieve from large databases those objects that best match the query specification, are usually processed by splitting them into a set of m simpler sub-queries, each dealing with only some of the query features. To determine which are the overall best-matching objects, a rule is then needed to integrate the results of such sub-queries, i.e., how to globally rank the m-dimensional vectors of matching degrees, or partial scores, that objects obtain on the m sub-queries. It is a fact that state-of-the-art approaches all adopt as integration rule a scoring function, such as weighted average, that aggregates the m partial scores into an overall (numerical) similarity score, so that objects can be linearly ordered and only the highest scored ones returned to the user. This choice however forces the system to compromise between the different sub-queries and can easily lead to miss relevant results. In this paper we explore the potentialities of a more general approach, based on the use of qualitative preferences, able to define arbitrary partial (rather than only linear) orders on database objects, so that a larger flexibility is gained in shaping what the user is looking for. For the purpose of efficient evaluation, we propose two integration algorithms able to work with any (monotone) partial order (thus also with scoring functions): MPO, which delivers objects one layer of the partial order at a time, and iMPO, which can incrementally return one object at a time, thus also suitable for processing top k queries. Our analysis demonstrates that using qualitative preferences pays off. In particular, using Skyline and Region-prioritized Skyline preferences for queries on a real image database, we show that the results we get have a precision comparable to that obtainable using scoring functions, yet they are obtained much faster, saving up to about 70% database accesses.

Scalable Content-Based Music Retrieval Using Chord Progression Histogram and Tree-Structure LSH

With more and more multimedia content made available on the Internet, music information retrieval is becoming a critical but challenging research topic, especially for real-time online search of similar songs from websites. In this paper we study how to quickly and reliably retrieve relevant songs from a large-scale dataset of music audio tracks according to melody similarity. Our contributions are two-fold: (i) Compact and accurate representation of audio tracks by exploiting music semantics. Chord progressions are recognized from audio signals based on trained music rules, and the recognition accuracy is improved by multi-probing. A concise chord progression histogram (CPH) is computed from each audio track as a mid-level feature, which retains the discriminative capability in describing audio content. (ii) Efficient organization of audio tracks according to their CPHs by using only one locality sensitive hash table with a tree-structure. A set of dominant chord progressions of each song is used as the hash key. Average degradation of ranks is further defined to estimate the similarity of two songs in terms of their dominant chord progressions, and used to control the number of probing in the retrieval stage. Experimental results on a large dataset with 74,055 music audio tracks confirm the scalability of the proposed retrieval algorithm. Compared to state-of-the-art methods, our algorithm improves the accuracy of summarization and indexing, and makes a further step towards the optimal performance determined by an exhaustive sequence comparison.