Time complexity of page filling algorithms in Materialized Aggregate List (MAL) and MAL/TRIGG materialization cost

The Materialized Aggregate List (MAL) enables effective storing and processing of long aggregates lists. The MAL structure contains an iterator table divided into pages that stores adequate number of aggregates. Time complexity of three algorithms was calculated and, in comparison with experimental results, the best configuration of MAL parameters (number of pages, single page size and number of database connections) was estimated. MAL can be also applied to every aggregation level in different indexing structures, like for instance the aR-tree

Research and Analysis of the Stream Materialized Aggregate List

Part 8: Information Technology: OLAP and Web ServicesInternational audienceThe problem of low-latency processing of large amounts of data acquired in continuously changing environment has led to the genesis of Stream Processing Systems (SPS). However, sometimes it is crucial to process both historical (archived) and current data, in order to obtain full knowledge about various phenomena. This is achieved in a Stream Data Warehouse (StrDW), where analytical operations on both historical and current data streams are performed. In this paper we focus on Stream Materialized Aggregate List (StrMAL) – a stream repository tier of StrDW. As a motivating example, the liquefied petrol storage and distribution system, containing continuous telemetric data acquisition, transmission and storage, will be presented as possible application for Stream Materialized Aggregate List

Physicochemical and thermophysical database (DAFIT) in the prospect of Java and data warehouse

In this paper, we are discussing the meaning of DaFiT database for knowledge on physicochemical properties, methods of database access and graphical presentation. The paper presents the state of development of DaFiT database and concentrates on the application of the Java and DW technologies

Mining Constrained Regions of Interest: An Optimization Approach

The amount and diversity of mobile and IoT location and trajectory data are increasing rapidly. As a consequence, there is an emerging need for flexible and scalable tools for analyzing this data. In this work we focus on an important building block for analyzing location data, that is, the problem of partitioning a space into regions of interest (ROIs) that are densely visited. The extraction of ROIs is of great importance as it constitutes the first step of many types of data analysis on mobility data, such as the extraction of trajectory patterns expressed in terms of sequences of ROIs. However, in this paper we argue that unconstrained ROIs are not meaningful and useful in all applications. To address this weakness, we propose the problem of constraint-based ROI mining, and identify two types of constraints: intra- and inter-ROI constraints. Subsequently, we propose an integer linear programming formulation of the task of discovering a fixed number of constrained ROIs from a binary density matrix. We extend the approach to discover automatically the number of ROIs by relying on the Minimum Description Length Principle. Our experiments on real data show that the approach is both flexible, scalable and able to retrieve constrained ROIs of higher quality than those extracted with existing approaches, even when no constraints are imposed