Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely solely on sensor proximity or road-network topology. This paper presents a spatio-temporal prediction framework, developed to incorporate knowledge in various forms. This framework aims to improve sensor-level, contextual understanding of the environment. A general-purpose knowledge graph (e.g., Wikidata) is used to create semantic subgraphs around traffic sensors and generate knowledge graph embeddings that capture meaningful relationships, such as nearby points of interest, administrative hierarchies, and the functional roles of locations. These embeddings are then fused with conventional traffic sensor graphs to provide additional adjacency matrices informed by semantics. This allows GNNs to learn the semantic context beyond physical connectivity. This study differs from previous research in two key ways. Firstly, rather than proposing a novel GNN architecture, it demonstrates the general impact of external knowledge on prediction accuracy. Secondly, experiments with well-established traffic forecasting approaches show that external knowledge provides additional information that street network data alone cannot convey. The results show that integrating data from general-purpose knowledge graphs and sensor networks through data fusion can enhance the prediction accuracy of traffic forecasting models, and offers a potential pathway toward improved interpretability.

Abstract The discovery of patterns in spatial and spatio-temporal data is crucial across scientific disciplines studying natural phenomena to enhance our understanding of the real world. These phenomena display complex patterns, necessitating novel specialized pattern mining techniques. In this paper, we introduce Vortex Correlation Clustering which aims to identify a subgroup of such complex pattern, namely correlated groups of objects oriented along a vortex. This can be achieved by adapting the Circle Hough Transform, already known from image analysis. The presented adaptations not only allow to cluster objects depending on their relative location next to each other, but also allows to take the orientation of individual objects into consideration. A multi-step approach allows to analyze and aggregate cluster candidates, allowing a certain deviation from the reference shape in the final clusters. Further adaptations allow to analyze clusters along a third dimension, which allows to reflect the shape of real-world objects in a three dimensional space. We evaluate our approach upon a real world application, to cluster particle simulations composing such shapes. Our approach outperforms comparable methods for this application, both in terms of effectiveness and efficiency. Additionally, we discuss how the adaptation enables further analysis capabilities. For instance, in the presented use case, the introduced approach allows to additionally analyze clusters throughout the depth of the water. So far, this is not feasible with existing approaches.

This paper addresses short-term Collision-Risk-Aware ship route planning while utilizing a deep learning-based Vessel Collision Risk Assessment and Forecasting (VCRA/F) framework to quantify risks. Lacking a clear boundary between risky and viable routes, we propose a Pareto-optimal search for alternative routes, balancing collision risk and voyage time. Our main contribution is a novel framework that integrates VCRA/F for Pareto-optimal route queries in dynamic environments. We model maritime routes using a hexagon-based graph network on the sea. Our experiments on real-world AIS data validate the effectiveness of Skyline-VCRA/F while highlighting areas for further improvement.

Archaeological excavations play a major role in gaining knowledge about prehistoric landscapes and ways of living. However, archaeological excavations are destructive acts and very resource intensive, so they cannot be performed in every area of interest. Therefore prospection methods have been developed, where feedbacks of e.g. lidar, radar or magnetic sensors are utilized to get an overview of the distribution, extent and complexity of underground structures in larger areas. After automated pre-processing of the sensor data arrays (and images) of these, grid data is provided as an input for exploration, analysis and annotation using geographic information system tools like QGIS. Annotating the images has been a fully manual task, performed by domain scientists. In this work we demonstrate a tool that supports domain scientists through automated annotation prediction. The implementation is integrated in the prevalent scientific workflow using available input and required output formats. The implementation is based on a pre-trained Rotated Retina Net. The manually annotated data of underground house remains from one of three archaeological sites is then used to pre-process and augment a feasible amount of training data for this specific task. One challenge was that global normalization of pixel values in the images did not yield useful results, because of modern infrastructure (such as utility pipes) distorting the magnetic feedback. A separated portion of the annotated data has been used for a quantitative evaluation of model performance. The system has also been applied to two additional, formerly unseen and non-annotated datasets where predicted annotations were found to be valuable for domain scientists. The system's output data can be used in GIS tools to edit annotations by experts, explore the sites, identify promising excavation sites and perform e.g. cluster analysis on house sizes and other features.

Mining spatio-temporal correlation patterns for traffic prediction is a well-studied field. However, most approaches are based on the assumption of the availability of and accessibility to a sufficiently dense data source, which is rather the rare case in reality. Traffic sensors in road networks are generally highly sparse in their distribution: fleet-based traffic sensing is sparse in space but also sparse in time. There are also other traffic application, besides road traffic, like moving objects in the marine space, where observations are sparsely and arbitrarily distributed in space. In this paper, we tackle the problem of traffic prediction on sparse and spatially irregular and non-deterministic traffic observations. We draw a border between imputations and this work as we consider high sparsity rates and no fixed sensor locations. We advance correlation mining methods with a Sparse Unstructured Spatio Temporal Reconstruction (SUSTeR) framework that reconstructs traffic states from sparse non-stationary observations. For the prediction the framework creates a hidden context traffic state which is enriched in a residual fashion with each observation. Such an assimilated hidden traffic state can be used by existing traffic prediction methods to predict future traffic states. We query these states with query locations from the spatial domain.

A special focus in data mining is to identify agglomerations of data points in spatial or spatio-temporal databases. Multiple applications have been presented to make use of such clustering algorithms. However, applications exist, where not only dense areas have to be identified, but also requirements regarding the correlation of the cluster to a specific shape must be met, i.e. circles. This is the case for eddy detection in marine science, where eddies are not only specified by their density, but also their circular-shaped rotation. Traditional clustering algorithms lack the ability to take such aspects into account.

Marine scientists investigate the movement of oceanic water particles with floating measurement devices released in the real ocean, as well as with virtual particles released in numerical model simulations. The detection, visualization, and evolution of clustered particles is key for gaining a comprehensive understanding of the underlying processes in the oceans. Thereby, vast amounts of mobility data (3D coordinates of these particles over time) need to be analyzed using mobility data science methods. In this paper, we describe the application of data science techniques to detect particle clusters and, more importantly, to track the evolution of these clusters over time in order to support the analysis of oceanic flows. In particular, we apply a well-known concept for tracking the cluster evolution from the data mining community that relies on pair-counting and, thus, is rather inefficient. In order to be applicable to large amounts of particles, we further elaborate two heuristic solutions to compute the cluster transitions based on spatial approximations. Experiments on real world data show a considerable speed-up while sacrificing marginal accuracy drops. Our prototype is used by domain experts for the analysis of the large-scale ocean by virtual particle release experiments in ocean simulations.

Damage detection in materials and structures plays a critical role in engineering and science applications like structural health monitoring. A particular challenge is presented by micro-scale cracks, which are imperceptible to the naked eye or in images, but may ultimately evolve into larger, potentially dangerous cracks. In this work, we propose spatio-temporal pattern recognition techniques to enable the detection of such imperceptible micro-cracks. In order to make these cracks detectable, we generate seismic waves on the surface area of interest and monitor how cracks interfere with the spatial propagation of the wave over time. On the resulting propagation image series we then apply segmentation techniques using deep encoder-decoder CNNs to predict the location of cracks, which otherwise could not be directly observed. Our solution is evaluated through extensive experiments on highly-realistic finite element simulations, which were developed by domain experts.

Quantitative information derived from scientific documents provides an important source of data for studies in almost all domains, however, manual extraction of this information is very time consuming. In this paper we will introduce a system Geo-Quantities that supports the automatic extraction of quantitative, spatial and temporal information of a given measurement entity from scientific literature using text mining techniques. The difficulty of automatic measurement recognition is mainly caused by the diverse expressions in the papers. Geo-Quantities offers an interactive interface for the visualization of extracted user-defined information, in particular spatial and temporal context. In our demonstration, we will showcase the capabilities of our system by retrieving measurements such as “mass accumulation rates” and “sedimentation rates” from scientific publications in the field of marine geology, which could have high impact in studies for building global mass accumulation rate maps. For training and evaluation of Geo-Quantities we use a corpus of domain-relevant papers.

The distribution of passively drifting particles within highly turbulent flows is a classic problem in marine sciences. The use of trajectory clustering on huge amounts of simulated marine trajectory data to identify main pathways of drifting particles has not been widely investigated from a data science perspective yet. In this paper, we propose a fast and computationally light method to efficiently identify main pathways in large amounts of trajectory data. It aims at overcoming some of the issues of probabilistic maps and existing trajectory clustering approaches. Our approach is evaluated against simulated larvae dispersion data based on a real-world model that have been produced as part of work in the marine science domain.

During the last decade, a variety of social networks and applications has been developed, providing to the users a set of potential functionalities. Thanks to these functionalities, they have become vital part of the daily life of many people. As a result, a great volume of data has been created. Due to the different nature of the functionalities, datasets of different nature and schema are created. This paper introduces GeoTe-Gra, a system that targets to reveal non-obvious knowledge by connecting datasets that derive from multiple heterogeneous sources. GeoTeGra is a scalable framework to compare different machine learning algorithms in terms of scalability and effectiveness, finding semantic similarities between entities. Our system is based on a distributed storage and parallel map-reduce manipulation for the fast retrieval of information from multi-class feature representations.

In this paper, we address the problem of optimizing information propagation in uncertain networks given a constrained budget of edges. We show that this problem requires to solve two NP-hard subproblems: the computation of expected information flow, and the optimal choice of edges. To compute the expected information flow to a source vertex, we propose the F-tree as a specialized data structure, that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independently of the remaining network.

Reliable propagation of information through large networks, e.g., communication networks, social networks, or sensor networks is very important in many applications concerning marketing, social networks, and wireless sensor networks. However, social ties of friendship may be obsolete, and communication links may fail, inducing the notion of uncertainty in such networks. In this paper, we address the problem of optimizing information propagation in uncertain networks given a constrained budget of edges. We show that this problem requires to solve two NP-hard subproblems: the computation of expected information flow, and the optimal choice of edges. To compute the expected information flow to a source vertex, we propose the F-tree as a specialized data structure, that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independently of the remaining network. For the problem of finding the optimal edges, we propose a series of heuristics that exploit properties of this data structure. Our evaluation shows that these heuristics lead to high quality solutions, thus yielding high information flow, while maintaining low running time.

Due to the availability of large transportation (e.g., road network sensor data) and transportation-related (e.g., pollution, crime) data as well as the ubiquity of car navigation systems, recent route planning techniques need to optimize for multiple criteria (e.g., travel time or distance, utility/value such as safety or attractiveness). In this paper, we introduce a novel problem called Twofold Time-Dependent Arc Orienteering Problem (2TD-AOP), which seeks to find a path from a source to a destination maximizing an accumulated value (e.g., attractiveness of the path) while not exceeding a cost budget (e.g., total travel time). 2TD-AOP has many applications in spatial crowdsourcing, real-time delivery, and online navigation systems (e.g., safest path, most scenic path). Although 2TD-AOP can be framed as a variant of AOP, existing AOP approaches cannot solve 2TD-AOP accurately as they assume that travel-times and values of network edges are constant. However, in real-world the travel-times and values are time-dependent, where the actual travel time and utility of an edge depend on the arrival time to the edge. We first discuss the practicality of this novel problem by demonstrating the benefits of considering time-dependency, empirically. Subsequently, we show that optimal solutions are infeasible (NP-hard) and solutions to the static problem are often invalid (i.e., exceed the cost budget). Therefore, we propose an efficient approximate solution with spatial pruning techniques, optimized for fast response systems. Experiments on a large-scale, fine-grained, real-world road network demonstrate that our approach always produces valid paths, is orders of magnitude faster than any optimal solution with acceptable accumulated value.

An inherent challenge arising in any dataset containing information of space and/or time is uncertainty due to various sources of imprecision. Integrating the impact of the uncertainty is a paramount when estimating the reliability (confidence) of any query result from the underlying input data. To deal with uncertainty, solutions have been proposed independently in the geo-science and the data-science research community. This interdisciplinary tutorial bridges the gap between the two communities by providing a comprehensive overview of the different challenges involved in dealing with uncertain geo-spatial data, by surveying solutions from both research communities, and by identifying similarities, synergies and open research problems.

The proliferation of the Web 2.0 and the ubiquitousness of social media yield a huge flood of heterogenous data that is voluntarily published and shared by billions of individual users all over the world. As a result, the representation of an entity (such as a real person) in this data may consist of various data types, including location and other numeric attributes, textual descriptions, images, videos, social network information and other types of information. Searching similar entities in this multi-enriched data exploiting the information of multiple representations simultaneously promises to yield more interesting and relevant information than searching among each data type individually. While efficient similarity search on single representations is a well studied problem, existing studies lacks appropriate solutions for multi-enriched data taking into account the combination of all representations as a whole. In this paper, we address the problem of index-supported similarity search on multi-enriched (a.k.a. multi-represented) objects based on a set of metrics, one metric for each representation. We define multimetric similarity search queries by employing user-defined weight function specifying the impact of each metric at query time. Our main contribution is an index structure which combines all metrics into a single multi-dimensional access method that works for arbitrary weights preferences. The experimental evaluation shows that our proposed index structure is more efficient than existing multi-metric access methods considering different cost criteria and tremendously outperforms traditional approaches when querying very large sets of multi-enriched objects.

Directions and paths, as commonly provided by route guidance systems, are usually derived considering absolute metrics, e.g., finding the shortest path within the underlying road network. This demo presents a framework which uses crowdsourced geospatial data to obtain paths that do not only minimize travel time but also guide users along popular points of interest (POIs). By analyzing textual travel blog data and Flickr data, we define a measure for popularity of POIs. This measure is used as an additional cost criterion in the underlying road network graph. Furthermore, we propose an approach to reduce the problem of finding paths which maximize popularity while minimizing travel time to the computation of bicriterion pareto optimal paths. The presented framework allows users to specify origin and destination within a road network, returning the set of pareto optimal paths or a subset thereof if a desired number of POIs along the path has been specified. Each of the returned routes is enriched with representative Flickr images and textual information from travel blogs. The framework and its results show that the computed paths yield competitive solutions in terms of travel time while also providing more “popular” paths, making routing easier and more informative for the user.

Fuzzy object databases are becoming more and more important in the context of image analysis. Examples include satellite images where blurred trees, houses or lakes can still be organized and searched in a meaningful manner and biomedical images which can be utilized to find similar disease patterns and monitor disease progress. One problem of the underlying data is that it contains blurred image content, i.e., fuzzy data. Therefore, an image-based similarity search, which can process huge amounts of fuzzy data in an efficient and effective way, is desirable. The aim of this work is to develop efficient and effective methods for similarity search in fuzzy object databases. First, a suitable similarity measure based on a shape similarity is proposed. Based on this, two novel k-nearest neighbor algorithms for efficient similarity search are presented. The first approach gains efficiency at the cost of incurring only approximate results, while the second approach uses a filter-refinement approach to prune computation. Our experimental evaluation shows the efficiency of the proposed algorithms.

A major concern when processing queries within a wireless sensor network is to minimize the energy consumption of the network nodes, thus extending the networks lifetime. One way to achieve this is by minimizing the amount of communication required to answer queries. In this paper we investigate exact continuous quantile queries, focusing on the particular case of the median query. Many recently proposed algorithms determine a quantile by performing a series of refining histogram queries. For that class of queries, we recently proposed a cost-model to estimate the optimal number of histogram buckets within an algorithm for mini-mizing the energy consumption of a query. In this paper, we extend that algorithm for continuous queries. Furthermore we also offer a new refinement-based algorithm that employs a heuristic to minimize the number of message transmis-sions. Our experiments, using synthetic and real datasets, show that despite its theoretical runtime complexity our heuristic solution is able to perform significantly better than histogram-based approaches. 1.

Directions and paths, as commonly provided by navigation systems, are usually derived considering absolute metrics, e.g., finding the shortest path within an underlying road network. With the aid of crowdsourced geospatial data we aim at obtaining paths that do not only minimize distance but also lead through more popular areas using knowledge generated by users. We extract spatial relations such as "nearby" or "next to" from geo-textual travel blogs, that define closeness between pairs of points of interest (POIs) and quantify each of these relations using a probabilistic model. Using Bayesian inference, we obtain a probabilistic measure of spatial closeness according to the crowd. Applying this measure to the corresponding road network, we derive an altered cost function taking crowdsourced spatial relations into account. We propose two routing algorithms on the enriched road networks. To evaluate our approach, we use Flickr photo data as a ground truth for popularity. Our experimental results -- based on real world datasets -- show that the computed paths yield competitive solutions in terms of path length while also providing more "popular" paths, making routing easier and more informative for the user.

Location-related data has a tremendous impact in many applications of high societal relevance and its growing volume from heterogeneous sources is one true example of a Big Data [1]. An inherent property of any spatio-temporal dataset is uncertainty due to various sources of imprecision. This tutorial provides a comprehensive overview of the different challenges involved in managing uncertain spatial and spatio-temporal data and presents state-of-the-art techniques for addressing them.

This paper targets the problem of computing meaningful clusterings from uncertain data sets. Existing methods for clustering uncertain data compute a single clustering without any indication of its quality and reliability; thus, decisions based on their results are questionable. In this paper, we describe a framework, based on possible-worlds semantics; when applied on an uncertain dataset, it computes a set of representative clusterings, each of which has a probabilistic guarantee not to exceed some maximum distance to the ground truth clustering, i.e., the clustering of the actual (but unknown) data. Our framework can be combined with any existing clustering algorithm and it is the first to provide quality guarantees about its result. In addition, our experimental evaluation shows that our representative clusterings have a much smaller deviation from the ground truth clustering than existing approaches, thus reducing the effect of uncertainty.

This demonstration presents our Uncertain-Spatio-Temporal (UST)} framework that we have developed in recent years. The framework allows not only to visualize and explore spatio-temporal data consisting of (location, time, object)-triples but also provides an extensive codebase easily extensible and customizable by developers and researchers. The main research focus of this UST-framework is the explicit consideration of uncertainty, an aspect that is inherent in spatio-temporal data, due to infrequent position updates, due to physical limitations and due to power constraints. The UST-framework can be used to obtain a deeper intuition of the quality of spatio-temporal data models. Such models aim at estimating the position of a spatio-temporal object at a time where the object's position is not explicitly known, for example by using both historic (traffic-) pattern information, and by using explicit observations of objects. The UST-framework illustrates the resulting distributions by allowing a user to move forward and backward in time. Additionally the framework allows users to specify simple spatio-temporal queries, such as spatio-temporal window queries and spatio-temporal nearest neighbor (NN) queries. Based on recently published theoretic concepts, the UST-framework allows to visually explore the impact of different models and parameters on spatio-temporal data. The main result showcased by the UST-framework is a minimization of uncertainty by employing stochastic processes, leading to small expected distances between ground truth trajectories and modelled positions.

This work tackles the management of novel types of inconsistencies in Spatio-Temporal Databases, different from traditional database settings where integrity constraints pertain to the explicitly stored (or, defined via views and aggregates) values. We observe that spatio-temporal data has its specific types of ßemanticconstraints and we aim at minimization of the changes needed for repairing their violations.

In Voronoi-based nearest neighbor search, the Voronoi cell of every point p in a database can be used to check whether p is the closest to some query point q. We extend the notion of Voronoi cells to support uncertain objects, whose attribute values are inexact. Particularly, we propose the Possible Voronoi cell (or PV-cell). A PV-cell of a multi-dimensional uncertain object o is a region R, such that for any point pϵR, o may be the nearest neighbor of p. If the PV-cells of all objects in a database S are known, they can be used to identify objects that have a chance to be the nearest neighbor of q. However, there is no efficient algorithm for computing an exact PV-cell. We hence study how to derive an axis-parallel hyper-rectangle (called the Uncertain Bounding Rectangle, or UBR) that tightly contains a PV-cell. We further develop the PV-index, a structure that stores UBRs, to evaluate probabilistic nearest neighbor queries over uncertain data. An advantage of the PV-index is that upon updates on S, it can be incrementally updated. Extensive experiments on both synthetic and real datasets are carried out to validate the performance of the PV-index.

In this paper we investigate how to efficiently and effectively use histogram queries for processing quantile queries in wireless sensor networks. A major concern when processing queries within such an environment is to minimize the energy consumption by the network nodes, thus extending the networks lifetime, e.g., the time when the first node runs out of energy. Towards that goal, we define a cost model for a refinement-based algorithm that performs a series of refining histogram queries in order to determine the exact quantile value. Given that the histogram size, i.e., its number of bins, is an important factor in the query processing cost, we use the defined cost model to estimate the histogram size that minimizes the maximum energy cost per-node when processing the quantile query. This is equivalent to maximizing the time until the first node dies and therefore to extending the network's lifetime. In our experiments, using synthetic and real datasets, we evaluate the performance of the proposed solutions in a variety of different settings.

Most algorithms treat Wireless Sensor Networks (WSNs) only as a generator of data without any autonomy. In contrast to this approach, we propose the ACIDE framework: A completely decentralized, bottom-up clustering process and information exchange that does not depend on given infrastructure such as fixed root nodes. While it has slightly higher requirements for the nodes, its dynamic and independent nature has many advantages, such as the user beeing able to initiate queries from any point in the network rather than being limited to query the network through an a priori fixed sink node. The framework can deal with changing environments and energy depletion. Through careful abstraction, we also support customization and adaption to different environments.

Data uncertainty is inherent in emerging applications such as location-based services, sensor monitoring systems, and data integration. To handle a large amount of imprecise information, uncertain databases have been recently developed. In this paper, we study how to efficiently discover frequent itemsets from large uncertain databases, interpreted under the Possible World Semantics. This is technically challenging, since an uncertain database induces an exponential number of possible worlds. To tackle this problem, we propose a novel methods to capture the itemset mining process as a probability distribution function taking two models into account: the Poisson distribution and the normal distribution. These model-based approaches extract frequent itemsets with a high degree of accuracy and support large databases. We apply our techniques to improve the performance of the algorithms for (1) finding itemsets whose frequentness probabilities are larger than some threshold and (2) mining itemsets with the $$k$$ highest frequentness probabilities. Our approaches support both tuple and attribute uncertainty models, which are commonly used to represent uncertain databases. Extensive evaluation on real and synthetic datasets shows that our methods are highly accurate and four orders of magnitudes faster than previous approaches. In further theoretical and experimental studies, we give an intuition which model-based approach fits best to different types of data sets.

Nearest neighbor (NN) queries in trajectory databases have received significant attention in the past, due to their applications in spatio-temporal 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.

Ranking queries have been investigated extensively in the past due to their broad range of applications. In this paper, we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas, such as biomedical image databases and GIS. To the best of our knowledge, we present the first efficient approach for similarity ranking in fuzzy object databases. The main challenge of ranking fuzzy objects is that these objects consist of multiple instances, each associated with a probability. We propose a framework to transform fuzzy objects into probabilistic objects which can then be ranked using existing algorithms for probabilistic objects.

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.

This demo presents a framework for running probabilistic graph queries on uncertain graphs and visualizing their results. The framework supports the most common uncertainty model for uncertain graphs, i.e. existential uncertainty for the edges of the graph. A large variety of meaningful graph queries are supported, such as shortest path, range, kN, reverse kN, reachability and various aggregation queries. Since the problem of exact probability computation according to possible world semantics is in #P-Time for many combinations of model and query, and since ignoring uncertainty (e.g. by using expectations only) will yield counterintuitive and hard to interpret results, our framework uses an optimized version of Monte-Carlo sampling to estimate the results which allows us not only to perform queries that conform to possible world semantics but also to sample only parts of a graph relevant for a given query. The main strength of this framework is the visualization combined with statistic hypothesis tests, which gives the user not only the estimated result of a query, but also an indication of how significant and reliable these results are. The aim of this demonstration is to give an intuition that a sampling based approach to probabilistic graphs is viable, and that the estimated results quickly converge even for very large graphs. A video demonstrating our framework can be downloaded at http://www.dbs.ifi.lmu.de/Publikationen/videos/PGraph.html

The problem of modeling and managing uncertain data has received a great deal of interest, due to its manifold applications in spatial, temporal, multimedia and sensor databases. There exists a wide range of work covering spatial uncertainty in the static (snapshot) case, where only one point of time is considered. In contrast, the problem of modeling and querying uncertain spatio-temporal data has only been treated as a simple extension of the spatial case, disregarding time dependencies between consecutive timestamps. In this work, we present a framework for efficiently modeling and querying uncertain spatio-temporal data. The key idea of our approach is to model possible object trajectories by stochastic processes. This approach has three major advantages over previous work. First it allows answering queries in accordance with the possible worlds model. Second, dependencies between object locations at consecutive points in time are taken into account. And third it is possible to reduce all queries on this model to simple matrix multiplications. Based on these concepts we propose efficient solutions for different probabilistic spatio-temporal queries. In an experimental evaluation we show that our approaches are several order of magnitudes faster than state-of-the-art competitors.

In this paper, we propose a novel, effective and efficient probabilistic pruning criterion for probabilistic similarity queries on uncertain data. Our approach supports a general uncertainty model using continuous probabilistic density functions to describe the (possibly correlated) uncertain attributes of objects. In a nutshell, the problem to be solved is to compute the PDF of the random variable denoted by the probabilistic domination count: Given an uncertain database object B, an uncertain reference object R and a set D of uncertain database objects in a multi-dimensional space, the probabilistic domination count denotes the number of uncertain objects in D that are closer to R than B. This domination count can be used to answer a wide range of probabilistic similarity queries. Specifically, we propose a novel geometric pruning filter and introduce an iterative filter-refinement strategy for conservatively and progressively estimating the probabilistic domination count in an efficient way while keeping correctness according to the possible world semantics. In an experimental evaluation, we show that our proposed technique allows to acquire tight probability bounds for the probabilistic domination count quickly, even for large uncertain databases.

Given a query object q , a reverse nearest neighbor (RNN) query in a common certain database returns the objects having q as their nearest neighbor. A new challenge for databases is dealing with uncertain objects. In this paper we consider probabilistic reverse nearest neighbor (PRNN) queries, which return the uncertain objects having the query object as nearest neighbor with a sufficiently high probability. We propose an algorithm for efficiently answering PRNN queries using new pruning mechanisms taking distance dependencies into account. We compare our algorithm to state-of-the-art approaches recently proposed. Our experimental evaluation shows that our approach is able to significantly outperform previous approaches. In addition, we show how our approach can easily be extended to PR k NN (where k > 1) query processing for which there is currently no efficient solution.

In this paper we focus on the problem of continuously monitoring the set of Reverse k-Nearest Neighbors (RkNNs) of a query object in a moving object database using a client server architecture. The RkNN monitoring query computes for a given query object q, the set RkNN(q) of objects having q as one of their k-nearest neighbors for each point in time. In our setting the central server can poll the exact positions of the clients if needed. However in contrast to most existing approaches for this problem we argue that in various applications, the limiting factor is not the computational time needed but the amount of traffic sent via the network. We propose an approach that minimizes the amount of communication between clients and central server by an intelligent approximation of the position of the clients. Additionally we propose several poll heuristics in order to further decrease the communication costs. In the experimental section we show the significant impact of our proposed improvements to our basic algorithm.

When searching for optimal paths in a network, algorithms like A*-search need an approximation of the minimal costs between the current node and a target node. A reference node embedding is a universal method for making such an approximation working for any type of positive edge weights. A drawback of the approach is that the memory consumption of the embedding is linearly increasing with the number of attributes and landmarks. In this paper, we propose methods for significantly decreasing the memory consumption of embedded graphs and examine the impact of the landmark selection.

Top-k queries are a popular type of query in wireless sensor networks. Typical solutions rely on coordinated root-to-nodes and nodes-to-root messages and on maintaining filters at the nodes, aiming at suppressing unnecessary messages, hence saving energy and furthering the network's lifetime. In this paper, we exploit the capability of a sensor node to cache a few recently observed values in order to determine "trends" for the observed values. Those trends can be used to further restrict the number of messages that need to be exchanged in the network, thus ultimately extending the network's lifetime. We compare our approach to the most recently proposed solutions in the literature using real and synthetic datasets, and we show that our approach is able to improve the network's lifetime by up to 28% without any loss in the quality of the answer.

This tutorial provides a comprehensive and comparative overview of general techniques to efficiently support similarity queries in spatial, temporal, spatio-temporal, and multimedia databases. In particular, it identifies the most generic query types and discusses general algorithmic methods to answer such queries efficiently. In addition, the tutorial sketches important applications of the introduced methods, and presents sample implementations of the general approaches within each of the aforementioned database types. The intended audience of this tutorial ranges from novice researchers to advanced experts as well as practitioners from any application domain dealing with spatial, temporal, spatio-temporal, and/or multimedia data.

In recent years, the research community introduced various methods for processing skyline queries in multidimensional databases. The skyline operator retrieves all objects being optimal w.r.t. an arbitrary linear weighting of the underlying criteria. The most prominent example query is to find a reasonable set of hotels which are cheap but close to the beach. In this paper, we propose an new approach for computing skylines on routes (paths) in a road network considering multiple preferences like distance, driving time, the number of traffic lights, gas consumption, etc. Since the consideration of different preferences usually involves different routes, a skyline-fashioned answer with relevant route candidates is highly useful. In our work, we employ graph embedding techniques to enable a best-first based graph exploration considering route preferences based on arbitrary road attributes. The core of our skyline query processor is a route iterator which iteratively computes the top routes according to (at least one) preference in an efficient way avoiding that route computations need to be issued from scratch in each iteration. Furthermore, we propose pruning techniques in order to reduce the search space. Our pruning strategies aim at pruning as many route candidates as possible during the graph exploration. Therefore, we are able to prune candidates which are only partially explored. Finally, we show that our approach is able to reduce the search space significantly and that the skyline can be computed in efficient time in our experimental evaluation.

Fast query processing of complex objects, e.g. spatial or uncertain objects, depends on efficient spatial pruning of objects' approximations, which are typically minimum bounding rectangles (MBRs). In this paper, we propose a novel effective and efficient criterion to determine the spatial topology between multi-dimensional rectangles. Given three rectangles R, A, and B, in a multi-dimensional space, the task is to determine whether A, is definitely closer to R, than B. This domination relation is used in many applications to perform spatial pruning. Traditional techniques apply spatial pruning based on minimal and maximal distance. These techniques however show significant deficiencies in terms of effectivity. We prove that our decision criterion is correct, complete, and efficient to compute even for high dimensional databases. In addition, we tackle the problem of computing the number of objects dominating an object o. The challenge here is to incorporate objects that only partially dominate o. In this work we will show how to detect such partial domination topology by using a modified version of our decision criterion. We propose strategies for conservatively and progressively estimating the total number of objects dominating an object. Our experiments show that the new pruning criterion, albeit very general and widely applicable, significantly outperforms current state-of-the-art pruning criteria.

Modern maps provide a variety of information about roads and their surrounding landscape allowing navigation systems to go beyond simple shortest path computation. In this demo, we show how the concept of skyline queries can be successfully adapted to routing problems considering multiple road attributes. In particular, we demonstrate how to compute several pareto-optimal paths which contain optimal results for a variety of user preferences. The PAROS-system has two main purposes. The first is to calculate the route skyline for a starting point and a destination. Our demonstrator visualizes the result set for up to three road attributes. Therefore, we provide a dual view on the computed skyline paths. The first view displays the result paths on the road map itself. The second view describes the result paths in the property space, displaying the trade-off between the underlying criteria. Thus, a user can browse through the results in order to find the path which fits best to his personal preferences. The second component of our system suits analysis issues. In this component, we illustrate the functionality of the underlying route skyline algorithm. Thus, we provide benchmark information about processing time and the search space visited during route skyline computation.

Managing, searching and mining uncertain data has achieved much attention in the database community recently due to new sensor technologies and new ways of collecting data. There is a number of challenges in terms of collecting, modelling, representing, querying, indexing and mining uncertain data. In its scope, the diversity of approaches addressing these topics is very high because the underlying assumptions of uncertainty are different across different papers. This tutorial provides a comprehensive and comparative overview of general techniques for the key topics in the fields of querying, indexing and mining uncertain data. In particular, it identifies the most generic types of probabilistic similarity queries and discusses general algorithmic methods to answer such queries efficiently. In addition, the tutorial sketches probabilistic methods for important data mining applications in the context of uncertain data with special emphasis on probabilistic clustering and probabilistic pattern mining. The intended audience of this tutorial ranges from novice researchers to advanced experts as well as practitioners from any application domain dealing with uncertain data retrieval and mining.

This paper introduces a scalable approach for probabilistic top-k similarity ranking on uncertain vector data. Each uncertain object is represented by a set of vector instances that is assumed to be mutually exclusive. The objective is to rank the uncertain data according to their distance to a reference object. We propose a framework that incrementally computes for each object instance and ranking position, the probability of the object falling at that ranking position. The resulting rank probability distribution can serve as input for several state-of-the-art probabilistic ranking models. Existing approaches compute this probability distribution by applying the Poisson binomial recurrence technique of quadratic complexity. In this paper, we theoretically as well as experimentally show that our framework reduces this to a linear-time complexity while having the same memory requirements, facilitated by incremental accessing of the uncertain vector instances in increasing order of their distance to the reference object. Furthermore, we show how the output of our method can be used to apply probabilistic top-k ranking for the objects, according to different state-of-the-art definitions. We conduct an experimental evaluation on synthetic and real data, which demonstrates the efficiency of our approach.

This demo describes the OSSOBOOK database system developed for archaeozoology applications providing data storage, data retrieval, and data mining facilities. It shows a case study of integrating state-of-the-art database concepts like intermittently synchronized database system as well as concepts of information retrieval and knowledge representation like similarity search and data mining in order to provide a comprehensive system for an interesting application domain.

In this paper, we propose an original solution for the general reverse k-nearest neighbor (RkNN) search problem. Compared to the limitations of existing methods for the RkNN search, our approach works on top of any hierarchically organized tree-like index structure and, thus, is applicable to any type of data as long as a metric distance function is defined on the data objects. We will exemplarily show how our approach works on top of the most prevalent index structures for Euclidean and metric data, the R-Tree and the M-Tree, respectively. Our solution is applicable for arbitrary values of k and can also be applied in dynamic environments where updates of the database frequently occur. Although being the most general solution for the RkNN problem, our solution outperforms existing methods in terms of query execution times because it exploits different strategies for pruning false drops and identifying true hits as soon as possible.

In this paper, we formalize the novel concept of Constrained Reverse k-Nearest Neighbor (CRkNN) search on mobile objects (clients) performed at a central server. The CRkNN query computes for a given query object q the set RkNN(q) of objects having q as one of their k-nearest neighbors, iff the result set exceeds a specific threshold m, i.e. Card(RkNN(q)) ≥ m. Otherwise, the query reports an empty result. In our setting, the positions of the query object and database objects are approximated by minimal bounding rectangles that depend on the last reported location of the object, as well as on the time that has been passed since the object reported its recent exact location. We propose an approach that minimizes the amount of communication between clients and central server by using the approximation of the positions to identify true hits and true drops. We present a multi-step filter/refinement framework that uses a novel refinement heuristic to minimize the number of objects that are required to provide their exact location. Our solution does not assume any preprocessing steps which makes it applicable for dynamic environments where updates of the database frequently occur. Experiments show that our approach considerably reduces the communication load compared to existing approaches designed for traditional reverse nearest neighbor search in static data.

In this paper, we formalize the novel concept of incremental reverse nearest neighbor ranking and suggest an original solution for this problem. We propose an efficient approach for reporting the results incrementally without the need to restart the search from scratch. Our approach can be applied to a multi-dimensional feature database which is hierarchically organized by any R-tree like index structure. Our solution does not assume any preprocessing steps which makes it applicable for dynamic environments where updates of the database frequently occur. Our experiments show that our approach reports the ranking results with much less page accesses than existing approaches designed for traditional reverse nearest neighbor search applied to the ranking problem.

Probabilistic frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied to standard "certain" transaction databases. The consideration of existential uncertainty of item(sets), indicating the probability that an item(set) occurs in a transaction, makes traditional techniques inapplicable. In this paper, we introduce new probabilistic formulations of frequent itemsets based on possible world semantics. In this probabilistic context, an itemset X is called frequent if the probability that X occurs in at least minSup transactions is above a given threshold τ. To the best of our knowledge, this is the first approach addressing this problem under possible worlds semantics. In consideration of the probabilistic formulations, we present a framework which is able to solve the Probabilistic Frequent Itemset Mining (PFIM) problem efficiently. An extensive experimental evaluation investigates the impact of our proposed techniques and shows that our approach is orders of magnitude faster than straight-forward approaches.

In this paper, we consider the following scenario: a set of mobile objects continuously track their positions in a road network and are able to communicate with a central server. The server which gets position updates from the moving objects has to detect the event that two objects reach or exceed a specified proximity distance. This way, the server is permanently aware of all pairs of objects that are within a certain distance range. Obviously, the communication costs between the objects and the server quickly become the bottleneck if a position update is sent to the server at each tracking time slot. We propose update strategies in order to reduce the communication overhead by defining special regions for each object. These regions are defined such that no position updates at the server are required as long as the objects do not leave their corresponding regions. We present efficient algorithms for updating these regions and detecting proximity/separation when objects leave their corresponding regions. Furthermore, we empirically evaluate the different strategies in terms of communication overhead, i.e. the number of required position updates.

Mining time series data is an important approach for the analysis in many application areas as diverse as biology, environmental research, medicine, or stock chart analysis. As nearly all data mining tasks on this kind of data depend on a distance function between two time series, a huge number of such functions has been developed during the last decades. The introduction of threshold-based distance functions presented a new concept of time series similarity and these functions were applied to data mining techniques on a wide spectrum of time series data. In this demonstration, we present the Java toolkit T-Time which is able to perform several data mining tasks for a complete range of threshold values in an interactive way. The results are visually presented in a very concise way so that the user can easily identify important threshold values. Combined with domain-specific knowledge, these pivotal values can yield novel insights beyond the means of the underlying data mining techniques the analysis is based on.

We present an original graph embedding to speedup distance-range andk-nearest neighbor queries on static and/or dynamic objects located on a (weighted) graph. Our method is used to compute a lower and upper bounding filter distance which approximates the true shortest path distance significantly better than traditional filters. In addition, we discuss how the computation of the exact shortest path distance in the refinement step can be boosted by using the embedded graph.

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.

In this paper, we present an original network graph embedding to speed-up distance-range and k-nearest neighbor queries in (weighted) graphs. Our approach implements the paradigm of filter-refinement query processing and can be used for proximity queries on both static as well as dynamic objects. In particular, we present how our embedding can be used to compute a lower and upper bounding filter distance which approximates the true shortest path distance significantly better than traditional filters, e.g. the Euclidean distance. These distance approximations can be used within a filter step to prune true drops and true hits as well as in the refinement step in order to guide an informed A* search. Our experimental evaluation on several real-world data sets demonstrates a significant performance boosting of our proposed concepts over existing work.

In this paper, we propose an approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our method uses an approximation of the nearest-neighbor-distances in order to prune the search space. In several experiments, our solution scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.

Similarity search in time series data is an active area of research. In this paper, we introduce the novel concept of threshold-similarity queries in time series databases which report those time series exceeding a user-defined query threshold at similar time frames compared to the query time series. In addition, we present a new data structure to support threshold similarity queries efficiently. The performance of our solution is demonstrated by an extensive experimental evaluation.

Location-based services and data mining algorithms analyzing objects moving on a complex traffic network are becoming increasingly important. In this paper, we introduce a new approach which effectively and efficiently detects dense areas in spatial networks. In an offline phase, we generate a hierarchical partitioning of the traffic network. Thereby, static entities like roads and buildings are likely to be in the same partitioning if they are close to each other according to their network distance. In the online phase, our prototype ViEWNet allows the effective and efficient monitoring of objects moving on a spatial network. Based on a clear visualization of the traffic intensity in each network cell, the user can easily detect hierarchies of dense areas by our powerful prototype ViEWNet.

The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, is a generalization of the reverse 1-nearest neighbor problem which has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and only a metric distance function is given for specifying object similarity. Usually, these applications need a solution for the generalized problem where the value of k is not known in advance and may change from query to query. However, existing approaches, except one, are designed for the specific R1NN problem. In addition - to the best of our knowledge - all previously proposed methods, especially the one for generalized RkNN search, are only applicable to Euclidean vector data but not for general metric objects. In this paper, we propose the first approach for efficient RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach uses the advantages of existing metric index structures but proposes to use conservative and progressive distance approximations in order to filter out true drops and true hits. In particular, we approximate the k-nearest neighbor distance for each data object by upper and lower bounds using two functions of only two parameters each. Thus, our method does not generate any considerable storage overhead. We show in a broad experimental evaluation on real-world data the scalability and the usability of our novel approach.

The issue of data mining in time series databases is of utmost importance for many practical applications and has attracted a lot of research in the past years. In this paper, we focus on the recently proposed concept of threshold similarity which compares the time series based on the time frames within which they exceed a user-defined amplitude threshold tau. We propose a novel approach for cluster analysis of time series based on adaptable threshold similarity. The most important issue in threshold similarity is the choice of the threshold tau. Thus, the threshold tau is automatically adapted to the characteristics of a small training dataset using the concept of support vector machines. Thus, the optimal tau is learned from a small training set in order to yield an accurate clustering of the entire time series database. In our experimental evaluation we demonstrate that our cluster analysis using adaptable threshold similarity can be successfully applied to many scientific real-world data mining applications

Subspace clustering has been investigated extensively since traditional clustering algorithms often fail to detect meaningful clusters in high-dimensional data spaces. Many recently proposed subspace clustering methods suffer from two severe problems: First, the algorithms typically scale exponentially with the data dimensionality and/or the subspace dimensionality of the clusters. Second, for performance reasons, many algorithms use a global density threshold for clustering, which is quite questionable since clusters in subspaces of significantly different dimensionality will most likely exhibit significantly varying densities. In this paper, we propose a generic framework to overcome these limitations. Our framework is based on an efficient filter-refinement architecture that scales at most quadratic w.r.t. the data dimensionality and the dimensionality of the subspace clusters. It can be applied to any clustering notions including notions that are based on a local density threshold. A broad experimental evaluation on synthetic and real-world data empirically shows that our method achieves a significant gain of runtime and quality in comparison to state-of-the-art subspace clustering algorithms.

Relational index structures, as for instance the Relational Interval Tree, the Relational RTree, or the Linear Quadtree, support efficient processing of queries on top of existing object-relational database systems. Furthermore, there exist effective and efficient models to estimate the selectivity and the I/O cost in order to guide the cost-based optimizer whether and how to include these index structures into the execution plan. By design, the models immediately fit to common extensible indexing/optimization frameworks, and their implementations exploit the built-in statistics facilities of the database server. In this paper, we show how these statistics can also be used for accelerating the access methods themselves by reducing the number of generated join partners. The different join partners are grouped together according to a cost-based grouping algorithm. Our first experiments on an Oracle9i database yield a speed-up of up to 1,000% for the Relational Interval Tree, the Relational R-Tree and for the Linear Quadtree.