Air pollution monitoring requires a large number of expensive devices, especially in large cities. To reduce the cost of this process, the use of mobile devices has been proposed. Some proposals promote the use of cheap sensors on board of public buses instead of just a limited number of specialized vehicles. Analyzing the data provided by these mobile devices is computationally costly and complex with traditional database tools. Instead, we propose using MobilityDB, a novel database implemented as an extension to PostgreSQL and PostGIS, which provides support for storing and querying geospatial trajectory data and their time-varying properties (like air-pollution parameters), implementing persistent database types and query operations for such data. We use public data from the city of Delhi to show the viability and advantages of our approach and how analytical queries are expressed in a concise and elegant way using MobilityDB data types and functions.

Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures that can be aggregated along dimensions. In a “Big Data” s cenario, traditional data warehousing and OLAP operations are clearly not sufficient to address current data analysis requirements, for example, social network analysis. Furthermore, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. Nevertheless, there is not much work on the problem of taking OLAP analysis to the graph data model. This paper proposes a formal multidimensional model for graph analysis, that considers the basic graph data, and also background information in the form of dimension hierarchies. The graphs in this model are node- and edge-labelled directed multi-hypergraphs, called graphoids, which can be defined at several different levels of granularity using the dimensions associated with them. Operations analogous to the ones used in typical OLAP over cubes are defined over graphoids. The paper presents a formal definition of the graphoid model for OLAP, proves that the typical OLAP operations on cubes can be expressed over the graphoid model, and shows that the classic data cube model is a particular case of the graphoid data model. Finally, a case study supports the claim that, for many kinds of OLAP-like analysis on graphs, the graphoid model works better than the typical relational OLAP alternative, and for the classic OLAP queries, it remains competitive.

A typical problem in the field of moving object (MO) databases consists in discovering interesting trajectory patterns. To solve this problem, data mining techniques are commonly used. Due to the huge volume of these trajectory data, some form of com

Nowadays, organizations need to use OLAP (On Line Analytical Processing) tools together with geographical information. To support this, the notion of SOLAP (Spatial OLAP) arouse, aimed at exploring spatial data in the same way as OLAP operates over tables. SOLAP however, only accounts for discrete spatial data. More sophisticated GIS-based decision support systems are increasingly being needed, to handle more complex types of data, like continuous fields. Fields describe physical phenomena that change continuously in time and/or space (e.g., temperature). Although many models have been proposed for adding spatial information to OLAP tools, no one allows the user to perceive data as a cube, and analyze any type of spatial data, continuous or discrete, together with typical alphanumerical discrete OLAP data, using only the classic OLAP operators (e.g., Roll-up, Drill-down). In this paper we propose an algebra that operates over data cubes, independently of the underlying data types and physical data representation. That means, in our approach, the final user only sees the typical OLAP operators at the query level. At lower abstraction levels we provide discrete and continuous spatial data support as well as different ways of partitioning the space. We also describe a proof-of-concept implementation to illustrate the ideas presented in the paper. As far as we are aware of, this is the first proposal that allows analyzing discrete and continuous spatiotemporal data and OLAP cubes together, using just the traditional OLAP operations, thus providing a very general framework for spatiotemporal data analysis.

Although raster data is the most popular discrete representation for continuous fields, other forms of space tessellation can be used, like Voronoi diagrams or Triangulated Irregular Networks (TIN). Besides, algebras for field manipulation have been proposed, but they are representation-dependent and not closed. To address this problems, we propose a (closed) generic map algebra over spatio-temporal continuous fields, independent of the underlying field representation. Based on this algebra, we define a language that allows analyzing continuous fields data and OLAP cubes together, using the traditional OLAP operations, providing a very general framework for spatio-temporal data analysis.

The classic Generalized Sequential Patterns (GSP) algorithm re-turns all frequent sequences present in a database. However, usu-ally a few ones are interesting from a user’s point of view. Thus, post-processing tasks are required in order to discard uninterest-ing sequences. To avoid this drawback, languages based on regular expressions (RE) were proposed to restrict frequent sequences to the ones that satisfy user-specified constraints. In all of these lan-guages, REs are applied over items, which limits their applicability in complex real-world situations. We propose a much powerful language, based on regular expressions, denoted RE-SPaM, where the basic elements are constraints defined over the (temporal and non-temporal) attributes of the items to be mined. Expressions in this language may include attributes, functions over attributes, and variables. We specify the syntax and semantics of RE-SPaM, and present a comprehensive set of examples to illustrate its expressive power. We study in detail how the expressions can be used to prune the resulting sequences in the mining process. In addition, we in-troduce techniques that allow pruning sequences in the early stages of the process, reducing the need to access the database, making use of the categorization of the attributes that compose the items, and of the automaton that accepts the language generated by the RE. Finally, we present experimental results. Although in this paper we focus on trajectory databases, our approach is general enough for being applied to other settings. 1.

Commercial Geographic Information Systems (GIS) still fail to provide integration with OLAP (On Line Analytical Processing) tools. In previous work we have introduced Piet, a system aimed at providing this integration. In this paper we describe a new powerful query language developed for the system, denoted Piet-QL. Piet-QL not only extends the original language for Piet, allowing for example to filter geometric queries with information in a data cube, but also has a more natural semantics. In this paper we formally study the syntax and semantics of Piet-QL, provide a comprehensive set of examples, describe the implementation, and discuss experimental results.

Data aggregation in Geographic Information Systems (GIS) is a desirable feature, although only marginally present in commercial systems, which also fail to provide integration between GIS and OLAP (On Line Analytical Processing). With this in mind, we have developed Piet, a system that makes use of a novel query processing technique: first, a process called sub-polygonization decomposes each thematic layer in a GIS, into open convex polygons; then, another process computes and stores in a database the overlay of those layers for later use by a query processor. We describe the implementation of Piet, and provide experimental evidence that overlay precomputation can outperform GIS systems that employ indexing schemes based on R-trees.