The analysis of time-oriented data is an important task in many application scenarios. In recent years, a variety of techniques for visualizing such data have been published. This variety makes it difficult for prospective users to select methods or tools that are useful for their particular task at hand. In this article, we develop and discuss a systematic view on the diversity of methods for visualizing time-oriented data. With the proposed categorization we try to untangle the visualization of time-oriented data, which is such an important concern in Visual Analytics. The categorization is not only helpful for users, but also for researchers to identify future tasks in Visual Analytics.

Abstract. Business Intelligence solutions, encompassing technologies such as multi-dimensional data modeling and aggregate query processing, are being applied increasingly to non-traditional data. This paper extends multi-dimensional aggregation to apply to data with associated interval values that capture when the data hold. In temporal databases, intervals typically capture the states of reality that the data apply to, or capture when the data are, or were, part of the current database state. This paper proposes a new aggregation operator that addresses several challenges posed by interval data. First, the intervals to be associated with the result tuples may not be known in advance, but depend on the actual data. Such unknown intervals are accommodated by allowing result groups that are specified only partially. Second, the operator contends with the case where an interval associated with data expresses that the data holds for each point in the interval, as well as the case where the data holds only for the entire interval, but must be adjusted to apply to sub-intervals. The paper reports on an implementation of the new operator and on an empirical study that indicates that the operator scales to large data sets and is competitive with respect to other temporal aggregation algorithms. 1

Spatial, temporal and spatio-temporal aggregates over continuous streams of remotely sensed image data build a fundamental operation in many applications in the environmental sciences. Several approaches to efficiently compute multi-dimensional aggregates have been proposed in the literature. However, none of these approaches is suitable to compute aggregate values over streaming raster image data where the spatial extents and positions of individual images vary over time. In particular, the computation of a single aggregate value becomes less meaningful when the image data contribute only partially to a query region. In this paper, we present an indexing scheme – based on the Box-Aggregation Tree – to efficiently compute spatiotemporal aggregates over streams of raster image data that vary in position and size. Using information about the spatial extent of incoming image data, we show how multiple aggregate values are computed for a single spatio-temporal query, thus providing more meaningful query results over spatially varying image data. Using National Oceanic and Atmospheric Administration’s (NOAA) Geostationary Operational Environmental Satellite (GOES) data, we show the feasibility and efficiency of the proposed approach.

High-level Video content analysis such as video-surveillance is often limited by computational aspects of automatic image understanding, i.e. it requires huge computing resources for reasoning processes like categorization and huge amount of data to represent knowledge of objects, scenarios and other models. This article explains how to design and develop a "near real-time adaptive image datamart", used, as a decisional support system for vision algorithms, and then as a mass storage system. Using RDF specification as storing format of vision algorithms meta-data, we can optimise the data warehouse concepts for video analysis, add some processes able to adapt the current model and pre-process data to speed-up queries. In this way, when new data is sent from a sensor to the data warehouse for long term storage, using remote procedure call embedded in object-oriented interfaces to simplified queries, they are processed and in memory data-model is updated. After some processing, possible interpretations of this data can be returned back to the sensor. To demonstrate this new approach, we will present typical scenarios applied to this architecture such as people tracking and events detection in a multi-camera network. Finally we will show how this system becomes a high-semantic data container for external data-mining.

For monitoring moving objects via wireless sensor networks, we introduce two aggregate query types: distinct entries to an area and the number of objects in that area. We present a new technique, Distributed Euler Histograms (DEHs), to store and query aggregated moving object data. Aggregate queries occur in a variety of applications ranging from wildlife monitoring to traffic management. We show that DEHs are significantly more efficient, in terms of communication and data storage costs, than techniques based on moving object identifiers and more accurate than techniques based on simple histograms. 1

This paper studies aggregate search in transaction time databases. Specifically, each object in such a database can be modeled as a horizontal segment, whose y-projection is its search key, and its x-projection represents the period when the key was valid in history. Given a query timestamp qt and a key range �qk, a count-query retrieves the number of objects that are alive at qt, and their keys fall in �qk. We provide a method that accurately answers such queries, with error less than 1 ε + ε · Nalive(qt), where Nalive(qt) is the number of objects alive at time qt, and ε is any constant in (0,1]. Denoting the disk page size as B, and n = N/B, our technique requires O(n) space, processes any query in O(logB n) time, and supports each update in O(logB n) amortized I/Os. As demonstrated by extensive experiments, the proposed solutions guarantee query results with extremely high precision (median relative error below 5%), while consuming only a fraction of the space occupied by the existing approaches that promise precise results. To appear in VLDB Journal.

Computer Science Geospatial image data obtained by satellites and aircraft are increasingly important to a wide range of applications, such as disaster management, climatol-ogy, and environmental monitoring. Spatio-temporal aggregations are some of the most important operations over such data. Because of the size of the data and the speed at which it is generated, computing such aggregates over geospatial image data is extremely demanding. Due to the special characteristics of the data, existing spatio-temporal aggregation models and evaluation approaches are not suitable for computing aggregates over such data. In this thesis, we analyze the characteristics of streaming geospatial image data and outline the key challenges of spatio-temporal aggregate computations. By showing that traditional aggregation models do not always provide an accurate view of the data, we propose new spatio-temporal aggregation models that infuse a more meaningful semantics into a query. More importantly, our experiments show that ex-isting approaches do not evaluate these queries efficiently. Existing approaches do not

In this paper we investigate some issues and solutions related to the design of a Data Warehouse (DW), storing several aggregate measures about trajectories of moving objects. First we discuss the loading phase of our DW which has to deal with overwhelming streams of trajectory observations, possibly produced at different rates, and arriving in an unpredictable and unbounded way. Then, we focus on the measure presence, the most complex measure stored in our DW. Such a measure returns the number of distinct trajectories that lie in a spatial region during a given temporal interval. We devise a novel way to compute an approximate, but very accurate, presence aggregate function, which algebraically combines a bounded amount of measures stored in the base cells of the data cube. We conducted many experiments to show the effectiveness of our method to compute such an aggregate function. In addition, the feasibility of our innovative trajectory DW was validated with an implementation based on Oracle. We investigated the most challenging issues in realizing our trajectory DW using standard DW technologies: namely, the preprocessing and loading phase, and the aggregation functions to support OLAP operations.

This chapter is concerned with multidimensional data models for spatial data warehouses. Over the last few years different approaches have been proposed in the literature for modelling multidimensional data with geometric extent. Nevertheless, the definition of a comprehensive and formal data model is still a major research issue. The main contributions of the chapter are twofold: First, it draws a picture of the research area; second it introduces a novel spatial multidimensional data model for spatial objects with geometry (MuSD – multigranular spatial data warehouse). MuSD complies with current standards for spatial data modelling, augmented by data warehousing concepts such as spatial fact, spatial dimension and spatial measure. The novelty of the model is the representation of spatial measures at multiple levels of geometric granularity. Besides the representation concepts, the model includes a set of OLAP operators supporting the navigation across dimension and measure levels. Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written

VESPA (Vehicular Event Sharing with a mobile P2P Architecture) 1 is a system for enabling vehicles to share information in vehicular ad-hoc networks (VANETs). The originality of VESPA is to process and disseminate any type of event (e.g., available parking spaces, accidents, emergency braking, information relative to the coordination of vehicles in emergency situations, etc.). The basic functions of VESPA are both disseminating events to potentially interested vehicles and evaluating their relevance once received in order to determine, for instance, whether the driver should be warned or not. This paper concentrates on knowledge extraction in VESPA. In particular, it focusses on how to exploit data exchanged among vehicles to produce knowledge to be used later on by drivers. Existing systems only use exchanged data to produce warnings for drivers when needed. Then, data is considered obsolete and is deleted. In contrast, we propose to aggregate data once it becomes “obsolete”. Our objective is to produce additional knowledge to be used by drivers when no relevant data has been communicated by neighboring vehicles. For example, by aggregating events it is possible to dynamically detect potentially dangerous road segments or to determine the areas where the probability to find an available parking space is high.