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31
On discovering moving clusters in spatio-temporal data
- In SSTD
, 2005
"... Abstract. A moving cluster is defined by a set of objects that move close to each other for a long time interval. Real-life examples are a group of migrating animals, a convoy of cars moving in a city, etc. We study the discovery of moving clusters in a database of object trajectories. The differenc ..."
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Abstract. A moving cluster is defined by a set of objects that move close to each other for a long time interval. Real-life examples are a group of migrating animals, a convoy of cars moving in a city, etc. We study the discovery of moving clusters in a database of object trajectories. The difference of this problem compared to clustering trajectories and mining movement patterns is that the identity of a moving cluster remains unchanged while its location and content may change over time. For example, while a group of animals are migrating, some animals may leave the group or new animals may enter it. We provide a formal definition for moving clusters and describe three algorithms for their automatic discovery: (i) a straight-forward method based on the definition, (ii) a more efficient method which avoids redundant checks and (iii) an approximate algorithm which trades accuracy for speed by borrowing ideas from the MPEG-2 video encoding. The experimental results demonstrate the efficiency of our techniques and their applicability to large spatio-temporal datasets. 1
Effective Density Queries on Continuously Moving Objects
- In ICDE
, 2006
"... This paper assumes a setting where a population of objects move continuously in the Euclidean plane. The position of each object, modeled as a linear function from time to points, is assumed known. In this setting, the paper studies the querying for dense regions. In particular, the paper defines a ..."
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This paper assumes a setting where a population of objects move continuously in the Euclidean plane. The position of each object, modeled as a linear function from time to points, is assumed known. In this setting, the paper studies the querying for dense regions. In particular, the paper defines a particular type of density query with desirable properties and then proceeds to propose an algorithm for the efficient computation of density queries. While the algorithm may exploit any existing index for the current and near-future positions of moving objects, the B x-tree is used. The paper reports on an extensive empirical study, which elicits the performance properties of the algorithm.
BerlinMOD: A Benchmark for Moving Object Databases
, 2007
"... This document presents a method to design scalable and representative moving object data (MOD) and a set of queries for benchmarking spatio-temporal DBMS. Instead of programming a dedicated generator software, we use the existing Secondo DBMS to create benchmark data. The benchmark is based on a sim ..."
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This document presents a method to design scalable and representative moving object data (MOD) and a set of queries for benchmarking spatio-temporal DBMS. Instead of programming a dedicated generator software, we use the existing Secondo DBMS to create benchmark data. The benchmark is based on a simulation scenario, where the positions of a sample of vehicles are observed for an arbitrary period of time within the street network of Berlin. We demonstrate the data generator’s extensibility by showing how to achieve more natural movement generation patterns, and how to disturb the vehicles’ positions to create noisy data. As an application and for reference, we also present first benchmarking results for the Secondo DBMS. Such a benchmark is useful in several ways: It provides well-defined data sets and queries for experimental evaluations; it simplifies experimental repeatability; it emphasizes the development of complete systems; it points out weaknesses in existing systems motivating further research. Moreover, the BerlinMOD benchmark allows one to compare different representations of the same moving objects.
Continuous Query Processing in Spatiotemporal Databases
- In Proceedings of the ICDE/EDBT PhD Workshop
, 2004
"... The tremendous increase of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous query processor designed specifically for highly dynamic environments (e.g., location-aware ..."
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The tremendous increase of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous query processor designed specifically for highly dynamic environments (e.g., location-aware environments). We implemented the proposed continuous query processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable location-aware database server currently developed at Purdue University. The PLACE server extends data streaming management systems to support location-aware environments. Such environments are characterized by the wide variety of continuous spatio-temporal queries and the unbounded spatio-temporal streams. The proposed continuous query processor mainly includes: (1) Developing new incremental spatio-temporal operators to support a wide variety of continuous spatio-temporal queries, (2) Extending the semantic of sliding window queries to deal with spatial sliding windows as well as temporal sliding windows, and (3) Providing a shared execution framework for scalable execution of a set of concurrent continuous spatio-temporal queries. Preliminary experimental evaluation shows the promising performance of the continuous query processor of the PLACE server.
Title Privacy-Preserving Data Mining on Moving Object Trajectories
, 2007
"... Any software made available via DB TECH REPORTS is provided “as is ” and without any express or implied warranties, including, without limitation, the implied warranty of merchantability and fitness for a particular purpose. The DB TECH REPORTS icon is made from two letters in an early version of th ..."
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Any software made available via DB TECH REPORTS is provided “as is ” and without any express or implied warranties, including, without limitation, the implied warranty of merchantability and fitness for a particular purpose. The DB TECH REPORTS icon is made from two letters in an early version of the Rune alphabet, which was used by the Vikings, among others. Runes have angular shapes and lack horizontal lines because the primary storage medium was wood, although they may also be found on jewelry, tools, and weapons. Runes were perceived as having magic, hidden powers. The first letter in the logo is “Dagaz, ” the rune for day or daylight and the phonetic equivalent of “d. ” Its meanings include happiness, activity, and satisfaction. The second letter is “Berkano, ” which is associated with the birch tree. Its divinatory meanings include health, new beginnings, growth, plenty, and clearance. It is associated with Idun, goddess of Spring, and with fertility. It is the phonetic equivalent of “b.” The popularity of embedded positioning technologies in mobile devices and the development of mobile communication technology have paved the way for powerful location-based services (LBSs). To make LBSs useful and user–friendly, heavy use is made of context information, including patterns in user location data which are extracted by data mining methods. However, there is a potential conflict of interest: the data mining methods
Spatial, Temporal and Spatio-Temporal Databases – Hot Issues and Directions for
- PhD Research. ACM SIGMOD Record
, 2004
"... Spatial and temporal database systems, both in theory and in practice, have developed dramatically over the past two decades to the point where usable commercial systems, underpinned by a robust theoretical foundation, are now starting to appear. While much remains to be done, topics for research mu ..."
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Spatial and temporal database systems, both in theory and in practice, have developed dramatically over the past two decades to the point where usable commercial systems, underpinned by a robust theoretical foundation, are now starting to appear. While much remains to be done, topics for research must be chosen carefully to avoid embarking on impractical or unprofitable areas. This is particularly true for doctoral research where the candidate must build a tangible contribution in a relatively short time. The panel session at the Eighth International Symposium on Spatial and Temporal Databases (SSTD 2003) held on Santorini Island, Greece [7] in July 2003 thus took as its focus the question What to focus on (and what to avoid) in Spatial and Temporal Database: recommendations for doctoral research. This short paper, authored by the panel members, summarizes these discussions.
Time-focused density-based clustering of trajectories of moving objects
- Miocene) and H. obscura (latest occurrence in the late Miocene). Fakahatchee Strand-Jones Grade core The Fakahatchee Strand-Jones Grade core (W-17394) was assigned U.S. Geological Survey Paleobotanical Number R5222. 11.4-12.0 ft depth (R5222 M) did
, 1986
"... Abstract. Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future, due to both technological and social/commercial reasons. From the data mining viewpoint, spatio-temporal trajectory data introduce new dimensions and, correspondingly, novel issues in perform ..."
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Abstract. Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future, due to both technological and social/commercial reasons. From the data mining viewpoint, spatio-temporal trajectory data introduce new dimensions and, correspondingly, novel issues in performing the analysis tasks. In this paper, we consider the clustering problem applied to the trajectory data domain. In particular, we propose an adaptation of a density-based clustering algorithm to trajectory data based on a simple notion of distance between trajectories. Then, a set of experiments on synthesized data is performed in order to test the algorithm and to compare it with other standard clustering approaches. Finally, a new approach to the trajectory clustering problem, called temporal focussing, is sketched, having the aim of exploiting the intrinsic semantics of the temporal dimension to improve the quality of trajectory clustering. Note: The authors are members of the Pisa KDD Laboratory, a joint research initiative
Statistical Density Prediction in Traffic Networks
, 2008
"... Recently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the traffic density in a network which offers valuable information for traffic control, congestion prediction and prevention. ..."
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Recently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the traffic density in a network which offers valuable information for traffic control, congestion prediction and prevention. In this paper, we propose a novel statistical approach to predict the density on any edge of such a network at some time in the future. Our method is based on short-time observations of the traffic history. Therefore, knowing the destination of each traveling individual is not required. Instead, we assume that the individuals will act rationally and choose the shortest path from their starting points to their destinations. Based on this assumption, we introduce a statistical approach to describe the likelihood of any given individual in the network to be located at a certain position at a certain time. Since determining this likelihood is quite expensive when done in a straightforward way, we propose an efficient method to speed up the prediction which is based on a suffix-tree. In our experiments, we show the capability of our approach to make useful predictions about the traffic density and illustrate the efficiency of our new algorithm when calculating these predictions.
Characterizing Dense Urban Areas from Mobile Phone-Call Data: Discovery and Social Dynamics
- 2ND INT. CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM2010)
, 2010
"... Abstract — The recent adoption of ubiquitous computing technologies (e.g. GPS, WLAN networks) has enabled capturing large amounts of spatio-temporal data about human motion. The digital footprints computed from these datasets provide complementary information for the study of social and human dynami ..."
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Abstract — The recent adoption of ubiquitous computing technologies (e.g. GPS, WLAN networks) has enabled capturing large amounts of spatio-temporal data about human motion. The digital footprints computed from these datasets provide complementary information for the study of social and human dynamics, with applications ranging from urban planning to transportation and epidemiology. A common problem for all these applications is the detection of dense areas, i.e. areas where individuals concentrate within a specific geographical region and time period. Nevertheless, the techniques used so far face an important limitation: they tend to identify as dense areas regions that do not respect the natural tessellation of the underlying space. In this paper, we propose a novel technique, called DAD-MST, to detect dense areas based on the Maximum Spanning Tree (MST) algorithm applied over the communication antennas of a cell phone infrastructure. We evaluate and validate our approach with a real dataset containing the Call Detail Records (CDR) of over one million individuals, and apply the methodology to study social dynamics in an urban environment. I.
