1,721,053 research outputs found
A Formal Algebra for OLAP
No full textOnline 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, where each cell contains one or more measures can be aggregated along dimensions. Despite the extensive corpus of work in the field, a standard language for OLAP is still needed, since there is no well-defined, accepted semantics, for many of the usual OLAP operations. In this paper, we address this problem, and present a set of operations for manipulating a data cube. We clearly de- fine the semantics of these operations, and prove that they can be composed, yielding a language powerful enough to express complex OLAP queries. We express these operations as a sequence of atomic transformations over a fixed multidimensional matrix, whose cells contain a sequence of measures. Each atomic transformation produces a new measure. When a sequence of transformations defines an OLAP operation, a flag is produced indicating which cells must be considered as input for the next operation. In this way, an elegant algebra is defined. Our main contribution, with respect to other similar efforts in the field is that, for the first time, a formal proof of the correctness of the operations is given, thus providing a clear semantics for them. We believe the present work will serve as a basis to build more solid practical tools for data analysis
OLAPing Graph Data
No full textOnline Analytical Processing (OLAP) comprises tools and algorithms that allow querying multi- dimensional 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” scenario, traditional data warehousing and OLAP operations on cubes are clearly not sufficient to address the current data analysis requirements, for example, for social network analysis. Furthermore, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation, like shortest-path, centrality analysis and so on. In spite of this, there is not much work on the problem of taking OLAP analysis to the graph data model.
In this paper, we propose a formal multidimensional data model for graph analysis, that consid- ers not only the basic graph data, but background information in the form of dimension hierarchies as well. The graphs in our model are node- and edge-labelled directed multi-hypergraphs, called graphoids, which can be defined at several different levels of granularity using the dimensions associ- ated with them. We define operations over this model, like the ones used in typical OLAP on cubes. We show that this model is more powerful than the traditional cube model, since hyperedges can connect a variable number of nodes, possibly of different types. This feature is not easy to represent in the OLAP cube model. Finally, we show that the classic data cube model is a particular case of the graphoid data model. As one of our main results, we prove that the graphoid-based operations are at least as powerful as the classical OLAP operations on cubes
OLAPing Graph Data
No full textOnline Analytical Processing (OLAP) comprises tools and algorithms that allow querying multi- dimensional 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” scenario, traditional data warehousing and OLAP operations on cubes are clearly not sufficient to address the current data analysis requirements, for example, for social network analysis. Furthermore, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation, like shortest-path, centrality analysis and so on. In spite of this, there is not much work on the problem of taking OLAP analysis to the graph data model.
In this paper, we propose a formal multidimensional data model for graph analysis, that consid- ers not only the basic graph data, but background information in the form of dimension hierarchies as well. The graphs in our model are node- and edge-labelled directed multi-hypergraphs, called graphoids, which can be defined at several different levels of granularity using the dimensions associ- ated with them. We define operations over this model, like the ones used in typical OLAP on cubes. We show that this model is more powerful than the traditional cube model, since hyperedges can connect a variable number of nodes, possibly of different types. This feature is not easy to represent in the OLAP cube model. Finally, we show that the classic data cube model is a particular case of the graphoid data model. As one of our main results, we prove that the graphoid-based operations are at least as powerful as the classical OLAP operations on cubes
A Formal Algebra for OLAP
No full textOnline 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, where each cell contains one or more measures can be aggregated along dimensions. Despite the extensive corpus of work in the field, a standard language for OLAP is still needed, since there is no well-defined, accepted semantics, for many of the usual OLAP operations. In this paper, we address this problem, and present a set of operations for manipulating a data cube. We clearly de- fine the semantics of these operations, and prove that they can be composed, yielding a language powerful enough to express complex OLAP queries. We express these operations as a sequence of atomic transformations over a fixed multidimensional matrix, whose cells contain a sequence of measures. Each atomic transformation produces a new measure. When a sequence of transformations defines an OLAP operation, a flag is produced indicating which cells must be considered as input for the next operation. In this way, an elegant algebra is defined. Our main contribution, with respect to other similar efforts in the field is that, for the first time, a formal proof of the correctness of the operations is given, thus providing a clear semantics for them. We believe the present work will serve as a basis to build more solid practical tools for data analysis
Performing OLAP over Graph Data: Query Language, Implementation, and a Case Study
No full textIn current “Big Data” scenarios, traditional data warehousing and Online Analytical Processing (OLAP) operations on cubes are clearly not sufficient to address the current data analysis requirements. Nevertheless, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. In spite of this, there is not much work on the problem of taking OLAP analysis to the graph data model. In previous work we proposed a multidimensional (MD) data model for graph analysis, that considers not only the basic graph data, but background information in the form of dimension hierarchies as well. The graphs in our model are node- and edgelabelled directed multi-hypergraphs, called graphoids, defined at several different levels of granularity. In this paper we show how we implemented this proposal over Neo4J, the most popular graph database nowadays, discuss implementation issues, and present a detailed case study to show how OLAP operations can be used on graphs
Online Analytical Processsing on Graph Data
No full textOnline 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" scenario, traditional data warehous-ing 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. In spite of this, 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. Graphoids can express, in a natural way, situations than imply relations between a variable number of dimensions, which is not easily done in the classical relational OLAP model. 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 remains competitive.Hasselt University (Korte verblijveninkomende mobiliteit) BOF16KV09
PICT-2014 Project 0787
PICT-2017 Project 105
Modeling and Querying Sensor Networks Using Temporal Graph Databases
No full textTransportation networks (e.g., river systems or road networks) equipped with sensors that collect data for several different purposes can be naturally modeled using graph databases. However, since networks can change over time, to represent these changes appropriately, a temporal graph data model is required. In this paper, we show that sensor-equipped transportation networks can be represented and queried using temporal graph databases and query languages. For this, we extend a recently introduced temporal graph data model and its high-level query language T-GQL to support time series in the nodes of the graph. We redefine temporal paths and study and implement a new kind of path, called Flow path. We take the Flanders' river system as a use case.Valeria Soliani and Alejandro Vaisman were partially supported by Project PICT 2017-1054, from the Argentinian Scientific Agency
Performing OLAP over Graph Data
No full textIn current “Big Data” scenarios, graph databases are increasingly being used. Online Analytical Processing (OLAP) operations can expand the possibilities of graph analysis beyond the traditional graph-based computation. We propose a multidimensional data model for graph analysis, that considers basic graph data and background information in the form of dimension hierarchies. The graphs in our 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. In this paper we describe this model, and its associated basic OLAP operations
Performing OLAP over Graph Data
No full textIn current “Big Data” scenarios, graph databases are increasingly being used. Online Analytical Processing (OLAP) operations can expand the possibilities of graph analysis beyond the traditional graph-based computation. We propose a multidimensional data model for graph analysis, that considers basic graph data and background information in the form of dimension hierarchies. The graphs in our 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. In this paper we describe this model, and its associated basic OLAP operations
Performing OLAP over Graph Data
Get PDFIn current Big Data scenarios, traditional data warehousing and Online Analytical Processing (OLAP) operations on cubes are clearly not sufficient to address the current data analysis requirements. Nevertheless, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. In spite of this, there is not much work on the problem of taking OLAP analysis to the graph data model. In previous work we proposed a multidimensional (MD) data model for graph analysis, that considers not only the basic graph data, but background information in the form of dimension hierarchies as well. The graphs in our model are node-and edge-labelled directed multi-hypergraphs, called graphoids, defined at several different levels of granularity. In this paper we show how we implemented this proposal over the widely used Neo4J graph database, discuss implementation issues, and present a detailed case study to show how OLAP operations can be used on graphs.Alejandro Vaisman was supported by a travel grant from Hasselt University (Korte verblijven-inkomende mobiliteit, BOF16KV09), and by the PICT-0787-2014 project
- …
