Model For On-Line Analytical Processing Research Articles

Pervasive Real-Time Analytical Framework—A Case Study on Car Parking Monitoring

Due to the amount of data emerging, it is necessary to use an online analytical processing (OLAP) framework capable of responding to the needs of industries. Processes such as drill-down, roll-up, three-dimensional analysis, and data filtering are fundamental for the perception of information. This article demonstrates the OLAP framework developed as a valuable and effective solution in decision making. To develop an OLAP framework, it was necessary to create the extract, transform and load the (ETL) process, build a data warehouse, and develop the OLAP via cube.js. Finally, it was essential to design a solution that adds more value to the organizations and presents several characteristics to support the entire data analysis process. A backend API (application programming interface) to route the data via MySQL was required, as well as a frontend and a data visualization layer. The OLAP framework was developed for the ioCity project. However, its great advantage is its versatility, which allows any industry to use it in its system. One ETL process, one data warehouse, one OLAP model, six indicators, and one OLAP framework were developed (with one frontend and one API backend). In conclusion, this article demonstrates the importance of a modular, adaptable, and scalable tool in the data analysis process and in supporting decision making.

Improving the Performance of Multidimensional Clinical Data for OLAP using an Optimized Data Clustering approach

Medicine is a fresh way to utilize for curing, analyzing and detecting the diseases through data clustering with OLAP (Online Analytical Processing). The large amount of multidimensional clinical data is reduced the efficiency of OLAP query processing by enhancing the query accessing time. Hence, the performance of OLAP model is improved by using data clustering in which huge data is divided into several groups (clusters) with cluster heads to achieve fast query processing in least time. In this paper, a Dragon Fly Optimization based Clustering (DFOC) approach is proposed to enhance the efficiency of data clustering by generating optimal clusters from multidimensional clinical data for OLAP. The results are evaluated on MATLAB 2019a tool and shown the better performance of DFOC against other clustering methods ACO, GA and K-Means in terms of intra-cluster distance, purity index, F-measure, and standard deviation

Online analytical processsing on graph data

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 scenario, 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.

The automatic creation of OLAP cube using an MDA approach

SummaryThe Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation.To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.

Analysis of task effort estimation accuracy based on use case point size

The use case point (UCP) method is one of the most commonly used size estimation methods in software development. Applicability of UCP size for the project effort estimation is thoroughly investigated; however, little attention is devoted to the effort estimation of particular task types. The authors have created and cross-compared prediction models for estimating task-type efforts by means of UCP size using an Online analytical processing model and R packages on a set of 32 real-world projects, with the goal of facilitating analysis of the correlation between project sizes and effort required to complete task types. Requirements, scoping, functional specification, and functional testing task types have up to two times better estimation accuracies than project effort. Implementation has slightly better accuracy than the project effort, while the other task types are not correlated to the UCP size. Using estimates of the most correlated task types and other techniques, such as expert judgment for others, we improved the overall project effort prediction accuracy and decreased the error from 26 to 16%.

Scalable graph-based OLAP analytics over process execution data

In today's knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of data from a variety of sources. In order to understand businesses one may need to perform considerable analytics over large hybrid collections of heterogeneous and partially unstructured data that is captured related to the process execution. This data, usually modeled as graphs, increasingly come to show all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics. We use the term big process graph to refer to such large hybrid collections of heterogeneous and partially unstructured process related execution data. Online analytical processing (OLAP) of big process graph is challenging as the extension of existing OLAP techniques to analysis of graphs is not straightforward. Moreover, process data analysis methods should be capable of processing and querying large amount of data effectively and efficiently, and therefore have to be able to scale well with the infrastructure's scale. While traditional analytics solutions (relational DBs, data warehouses and OLAP), do a great job in collecting data and providing answers on known questions, key business insights remain hidden in the interactions among objects: it will be hard to discover concept hierarchies for entities based on both data objects and their interactions in process graphs. In this paper, we introduce a framework and a set of methods to support scalable graph-based OLAP analytics over process execution data. The goal is to facilitate the analytics over big process graph through summarizing the process graph and providing multiple views at different granularity. To achieve this goal, we present a model for process OLAP (P-OLAP) and define OLAP specific abstractions in process context such as process cubes, dimensions, and cells. We present a MapReduce-based graph processing engine, to support big data analytics over process graphs. We have implemented the P-OLAP framework and integrated it into our existing process data analytics platform, ProcessAtlas, which introduces a scalable architecture for querying, exploration and analysis of large process data. We report on experiments performed on both synthetic and real-world datasets that show the viability and efficiency of the approach.

MiNT-OLAP cluster: minimizing network transmission cost in OLAP cluster for main memory analytical database

Powerful storage, high performance and scalability are the most important issues for analytical databases. These three factors interact with each other, for example, powerful storage needs less scalability but higher performance, high performance means less consumption of indexes and other materializations for storage and fewer processing nodes, larger scale relieves stress on powerful storage and the high performance processing engine. Some analytical databases (ParAccel, Teradata) bind their performance with advanced hardware supports, some (Asterdata, Greenplum) rely on the high scalability framework of MapReduce, some (MonetDB, Sybase IQ, Vertica) highlight performance on processing engine and storage engine. All these approaches can be integrated into an storage-performance-scalability (S-P-S) model, and future large scale analytical processing can be built on moderate clusters to minimize expensive hardware dependency. The most important thing is a simple software framework is fundamental to maintain pace with the development of hardware technologies. In this paper, we propose a schema-aware on-line analytical processing (OLAP) model with deep optimization from native features of the star or snowflake schema. The OLAP model divides the whole process into several stages, each stage pipes its output to the next stage, we minimize the size of output data in each stage, whether in central processing or clustered processing. We extend this mechanism to cluster processing using two major techniques, one is using NetMemory as a broadcasting protocol based dimension mirror synchronizing buffer, the other is predicate-vector based DDTA-OLAP cluster model which can minimize the data dependency of star-join using bitmap vectors. Our OLAP model aims to minimize network transmission cost (MiNT in short) for OLAP clusters and support a scalable but simple distributed storagemodel for large scale clustering processing. Finally, the experimental results show the speedup and scalability performance.

PRIVACY-PRESERVING OLAP FOR ACCURATE ANSWER

Recently, growing privacy concerns have received more and more attention and it becomes a significant topic on how to preserve private-sensitive information from being violated in distributed cooperative computation. In this paper, we first propose a novel-general privacy-preserving online analytical processing model based on secure multiparty computation. Then, based on the new model, two schemes to privacy-preserving count aggregate query over both horizontally partitioned data and vertically partitioned data are proposed. Additionally, we also propose several efficient subprotocols that serve as the basic secure buildings. Furthermore, we analyze correctness, security, communication cost, and computation complexity of our proposed protocols, and show that the new schemes are secure, having good linear complexity and that the query results are exactly accurate.

PENGEMBANGAN DATA WAREHOUSE DAN ON-LINE ANALYTICAL PROCESSING (OLAP) UNTUK PENEMUAN INFORMASI DAN ANALISIS DATA (Studi Kasus : Sistem Informasi Penerimaan Mahasiswa Baru STMIK AMIKOM PURWOKERTO)

Background preparation of this thesis report, Silva (2007) mentions that the competition for students between universities was inevitable as it grows, universities should equip themselves to survive in the competition that ultimately colleges that are not sensitive to compete. The demand for a high performance organization that has also faced by STMIK AMIKOM Purwokerto. Existing data on the colleges that are so big and much needed storage space allocation will be helped and be efficient with the data warehouse (Wikramanayake, 2006). Based on the data management / decision makers in education institutions is always a constant attempt to find an information / decision analysis that are useful in the search for new students. For the needs of the analysis method was used OLAP (OnLine Analytical Processing) which presents the data in the form of multidimensional query that is easier to be presented and analyzed. Data collection methods used in the preparation of this report include the method of observation of interviews and field studies, literature, internet search, as well as observing systems that are already running. This research was conducted with the following steps: 1) The analysis system that includes the current SIPMB analysis, identification of the cause of the problem, analysis of system requirements. 2) System design, including design of data warehouses and OLAP applications PMB. This design produces data warehouse architecture, data modeling dimensional, the establishment of a data warehouse, OLAP stakeholders PMB, PMB OLAP viewpoint, scenario, model systems, and user interface design. 3) Implementation, covers the application of OLAP applications PMB and operations performed included, 4) testing system, test the product in accordance with ISO 9126 software includes six quality characteristics of Functionality, Reliability, Usability, efficiency, Maintainability, Portability. In order not to interfere the performance of the operational databases (OLTP) SIPMB process of creating a data warehouse is done on different machines by performing replicate of the database used SIPMB. For the needs of the application is made data analysis OLAP PMB. These applications could present multidimensional data in grid view. Analysis of these data may include analysis of specialization in new student enrollment based on the period of new admissions, enrollment surge, home school, home province and district, Pengembangan Data Warehouse dan On-line Analytical Processing (OLAP) Untuk Penemuan Informasi Dan Analisis Data Jurnal Telematika Vol. 4 No.2 Agustus 2011 14 registration information, day, month, or year. So that the results of data analysis can be the management in determining the appropriate marketing strategies to increase the number of freshmen applicants either currently running or for admission in the coming year.

The integration of business intelligence and knowledge management

Enterprise executives understand that timely, accurate knowledge can mean improved business performance. Two technologies have been central in improving the quantitative and qualitative value of the knowledge available to decision makers: business intelligence and knowledge management. Business intelligence has applied the functionality, scalability, and reliability of modern database management systems to build ever-larger data warehouses, and to utilize data mining techniques to extract business advantage from the vast amount of available enterprise data. Knowledge management technologies, while less mature than business intelligence technologies, are now capable of combining today's content management systems and the Web with vastly improved searching and text mining capabilities to derive more value from the explosion of textual information. We believe that these systems will blend over time, borrowing techniques from each other and inspiring new approaches that can analyze data and text together, seamlessly. We call this blended technology BIKM. In this paper, we describe some of the current business problems that require analysis of both text and data, and some of the technical challenges posed by these problems. We describe a particular approach based on an OLAP (on-line analytical processing) model enhanced with text analysis, and describe two tools that we have developed to explore this approach--eClassifier performs text analysis, and Sapient integrates data and text through an OLAP-style interaction model. Finally, we discuss some new research that we are pursuing to enhance this approach.

A Conceptual Model and Algebra for On-Line Analytical Processing in Decision Support Databases

Data warehousing and On-Line Analytical Processing (OLAP) are two of the most significant new technologies in the business data processing arena. A data warehouse, or decision support database, can be defined as a “very large” repository of historical data pertaining to an organization. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. The complexity of queries required to support OLAP applications makes it difficult to implement using standard relational database technology. Moreover, currently there is no standard conceptual model for OLAP. There clearly is a need for such a model and an algebra as evidenced by the numerous SQL extensions offered by many vendors of OLAP products. In this paper we address this issue by proposing a model of a data cube and an algebra to support OLAP operations on this cube. The model we present is simple and intuitive, and the algebra provides a means to concisely express complex OLAP queries.

The cube data model: a conceptual model and algebra for on-line analytical processing in data warehouses

Data warehousing and On-Line Analytical Processing (OLAP) are two of the most significant new technologies in the business data processing arena. A data warehouse can be defined as a “very large” repository of historical data pertaining to an organization. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. The complexity of queries required to support OLAP applications makes it difficult to implement using standard relational database technology. Moreover, there is currently no standard conceptual model for OLAP. There is clearly a need for such a model and an algebra as evidenced by the numerous SQL extensions offered by many vendors of OLAP products. In this paper, we address this issue by proposing a model of a data cube and an algebra to support OLAP operations on this cube. The model we present is simple and intuitive, and the algebra provides a means to concisely express complex OLAP queries.