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A TemporalProbabilistic Database Model for Information Extraction
"... Temporal annotations of facts are a key component both for building a high-accuracy knowledge base and for answering queries over the resulting temporal knowledge base with high precision and recall. In this paper, we present a temporalprobabilistic database model for cleaning uncertain temporal fac ..."
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Cited by 7 (2 self)
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Temporal annotations of facts are a key component both for building a high-accuracy knowledge base and for answering queries over the resulting temporal knowledge base with high precision and recall. In this paper, we present a temporalprobabilistic database model for cleaning uncertain temporal facts obtained from information extraction methods. Specifically, we consider a combination of temporal deduction rules, temporal consistency constraints and probabilistic inference based on the common possible-worlds semantics with data lineage, and we study the theoretical properties of this data model. We further develop a query engine which is capable of scaling to very large temporal knowledge bases, with nearly interactive query response times over millions of uncertain facts and hundreds of thousands of grounded rules. Our experiments over two real-world datasets demonstrate the increased robustness of our approach compared to related techniques based on constraint solving via Integer Linear Programming (ILP) and probabilistic inference via Markov Logic Networks (MLNs). We are also able to show that our runtime performance is more than competitive to current ILP solvers and the fastest available, probabilistic but non-temporal, database engines. 1.
Probabilistic Nearest Neighbor Queries on Uncertain Moving Object Trajectories
, 2013
"... Nearest neighbor (NN) queries in trajectory databases have received significant attention in the past, due to their applications in spatiotemporal data analysis. More recent work has considered the realistic case where the trajectories are uncertain; however, only simple uncertainty models have been ..."
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Cited by 4 (2 self)
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Nearest neighbor (NN) queries in trajectory databases have received significant attention in the past, due to their applications in spatiotemporal data analysis. More recent work has considered the realistic case where the trajectories are uncertain; however, only simple uncertainty models have been proposed, which do not allow for accurate probabilistic search. In this paper, we fill this gap by addressing probabilistic nearest neighbor queries in databases with uncertain trajectories modeled by stochastic processes, specifically the Markov chain model. We study three nearest neighbor query semantics that take as input a query state or trajectory q and a time interval, and theoretically evaluate their runtime complexity. Furthermore we propose a sampling approach which uses Bayesian inference to guarantee that sampled trajectories conform to the observation data stored in the database. This sampling approach can be used in Monte-Carlo based approximation solutions. We include an extensive experimental study to support our theoretical results.
Indexing Uncertain Spatiotemporal Data
"... representing uncertain spatiotemporal data The advances in sensing and telecommunication technologies allow the collection and management of vast amounts of spatio-temporal data combining location and time information. Due to physical and resource limitations of data collection devices (e.g., RFID r ..."
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Cited by 3 (2 self)
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representing uncertain spatiotemporal data The advances in sensing and telecommunication technologies allow the collection and management of vast amounts of spatio-temporal data combining location and time information. Due to physical and resource limitations of data collection devices (e.g., RFID readers, GPS receivers and other sensors) data are typically collected only at discrete points of time. In-between these discrete time instances, the positions of tracked moving objects are uncertain. In this work, we propose novel approximation techniques in order to probabilistically bound the uncertain movement of objects; these techniques allow for efficient and effective filtering during query evaluation using an hierarchical index structure. To the best of our knowledge, this is the first approach that supports query evaluation on very large uncertain spatio-temporal databases, adhering to possible worlds semantics. We experimentally show that it accelerates the existing, scan-based approach by orders of magnitude.
Probabilistic Range Query over Uncertain Moving Objects in Constrained Two-dimensional Space
, 2013
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Reverse-Nearest Neighbor Queries on Uncertain Moving Object Trajectories
, 2014
"... Reverse nearest neighbor (RNN) queries in spatial and spatio-temporal databases have received significant attention in the database research community over the last decade. A reverse nearest neighbor (RNN) query finds the objects having a given query object as its nearest neighbor. RNN queries find ..."
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Reverse nearest neighbor (RNN) queries in spatial and spatio-temporal databases have received significant attention in the database research community over the last decade. A reverse nearest neighbor (RNN) query finds the objects having a given query object as its nearest neighbor. RNN queries find applications in data mining, marketing analysis, and decision making. Most previous research on RNN queries over trajectory databases assume that the data are certain. In realistic scenarios, however, trajectories are inherently uncertain due to measure-ment errors or time-discretized sampling. In this paper, we study RNN queries in databases of uncertain trajectories. We propose two types of RNN queries based on a well established model for uncertain spatial temporal data based on stochas-tic processes, namely the Markov model. To the best of our knowledge our work is the first to consider RNN queries on uncertain trajectory databases in accor-dance with the possible worlds semantics. We include an extensive experimental evaluation on both real and synthetic data sets to verify our theoretical results.
Efficient Tracking and Querying for Coordinated Uncertain Mobile Objects
"... Abstract — Accurately estimating the current positions of moving objects is a challenging task due to the various forms of data uncertainty (e.g. limited sensor precision, periodic updates from continuously moving objects). However, in many cases, groups of objects tend to exhibit similarities in th ..."
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Abstract — Accurately estimating the current positions of moving objects is a challenging task due to the various forms of data uncertainty (e.g. limited sensor precision, periodic updates from continuously moving objects). However, in many cases, groups of objects tend to exhibit similarities in their movement behavior. For example, vehicles in a convoy or animals in a herd both exhibit tightly coupled movement behavior within the group. While such statistical dependencies often increase the computational complexity necessary for capturing this additional structure, they also provide useful information which can be utilized to provide more accurate location estimates. In this paper, we propose a novel model for accurately tracking coordinated groups of mobile uncertain objects. We introduce an exact and more efficient approximate inference algorithm for updating the current location of each object upon the arrival of new (uncertain) location observations. Additionally, we derive probability bounds over the groups in order to process probabilistic threshold range queries more efficiently. Our experimental evaluation shows that our proposed model can provide 4X improvements in tracking accuracy over competing models which do not consider group behavior. We also show that our bounds enable us to prune up to 50 % of the database, resulting in more efficient processing over a linear scan. I.
Uncertain Voronoi Cell Computation based on Space Decomposition
"... Abstract. The problem of computing Voronoi cells for spatial objects whose lo-cations are not certain has been recently studied. In this work, we propose a new approach to compute Voronoi cells for the case of objects having rectangular un-certainty regions. Since exact computation of Voronoi cells ..."
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Abstract. The problem of computing Voronoi cells for spatial objects whose lo-cations are not certain has been recently studied. In this work, we propose a new approach to compute Voronoi cells for the case of objects having rectangular un-certainty regions. Since exact computation of Voronoi cells is hard, we propose an approximate solution. The main idea of this solution is to apply hierarchical ac-cess methods for both data and object space. Our space index is used to efficiently find spatial regions which must (not) be inside a Voronoi cell. Our object index is used to efficiently identify Delauny relations, i.e., data objects which affect the shape of a Voronoi cell. We develop three algorithms to explore index structures and show that the approach that descends both index structures in parallel yields fast query processing times. Our experiments show that we are able to approxi-mate uncertain Voronoi cells much more effectively than the state-of-the-art, and at the same time, improve run-time performance. 1
Dynamic Differential Privacy for Location based Applications
"... Concerns on location privacy frequently arise with the rapid development of GPS enabled devices and location-based ap-plications. While spatial transformation techniques such as location perturbation or generalization have been stud-ied extensively, most techniques rely on syntactic privacy models w ..."
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Concerns on location privacy frequently arise with the rapid development of GPS enabled devices and location-based ap-plications. While spatial transformation techniques such as location perturbation or generalization have been stud-ied extensively, most techniques rely on syntactic privacy models without rigorous privacy guarantee. Many of them only consider static scenarios or perturb the location at sin-gle timestamps without considering temporal correlations of a moving user’s locations, and hence are vulnerable to various inference attacks. While differential privacy has been accepted as a de facto standard for privacy protection, applying differential privacy in location based applications presents new challenges, as the protection needs to be en-forced on the fly for a single user and needs to incorporate temporal correlations between a user’s locations. In this paper, we propose a systematic solution for contin-ual location sharing with rigorous differential privacy guar-antee. First, we propose a framework of dynamic differen-tial privacy to account for adversarial knowledge of temporal correlations between a user’s locations modeled through hid-den Markov model. Second, we present a planar isotropic mechanism to achieve the dynamic differential privacy. In-stead of using the well known `1 norm sensitivity, we propose a new notion, sensitivity hull, to represent the sensitivities of all dimensions together. We also prove that the error bound of this problem is determined by the sensitivity hull, based on which the optimality of the proposed algorithm is shown, analytically and experimentally. 1.
Similarity Search on Uncertain Spatio-Temporal Data
"... Abstract: In this work, we address the problem of similarity search in a database of uncertain spatio-temporal objects. Each object is defined by a set of observations ((time,location)-tuples) and a Markov chain which describes the objects uncertain mo-tion in space and time. To model similarity- wh ..."
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Abstract: In this work, we address the problem of similarity search in a database of uncertain spatio-temporal objects. Each object is defined by a set of observations ((time,location)-tuples) and a Markov chain which describes the objects uncertain mo-tion in space and time. To model similarity- which is an important building block for many applications such as identifying frequent motion patterns or trajectory clus-tering- we employ the well-known Longest Common Subsequence (LCSS) measure, which becomes a distribution on uncertain spatio-temporal data (ULCSS). We show how the aligned version (without time shifting) of the ULCSS can be exactly computed in PTIME, which is also verified by extensive experiments. 1
Predictive Nearest Neighbor Queries Over Uncertain Spatial-Temporal Data
"... Abstract. Predictive nearest neighbor queries over spatial-temporal da-ta have received significant attention in many location-based services including intelligent transportation, ride sharing and advertising. Due to physical and resource limitations of data collection devices like R-FID, sensors an ..."
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Abstract. Predictive nearest neighbor queries over spatial-temporal da-ta have received significant attention in many location-based services including intelligent transportation, ride sharing and advertising. Due to physical and resource limitations of data collection devices like R-FID, sensors and GPS, data is collected only at discrete time instants. In-between these discrete time instants, the positions of the monitored moving objects are uncertain. In this paper, we exploit the filtering and refining framework to solve the predictive nearest neighbor queries over uncertain spatial-temporal data. Specifically, in the filter phase, our ap-proach employs a semi-Markov process model that describes object mo-bility between space grids and prunes those objects that have zero prob-ability to encounter the queried object. In the refining phase, we use a Markov chain model to describe the mobility of moving objects between space points and compute the nearest neighbor probability for each can-didate object. We experimentally show that our approach can filter out most of the impossible objects and has a good predication performance.
