Online Analytical Processing Techniques Research Articles

Temporal Graph Cube

Data warehouse and OLAP (Online Analytical Processing) are effective tools for decision support on traditional relational data and static multidimensional network data. However, many real-world multidimensional networks are often modeled as temporal multidimensional networks, where the edges in the network are associated with temporal information. Such temporal multidimensional networks typically cannot be handled by traditional data warehouse and OLAP techniques. To fill this gap, we propose a novel data warehouse model, named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {Temporal{\kern3.0pt}Graph{\kern3.0pt}Cube}$</tex-math></inline-formula> , to support OLAP queries on temporal multidimensional networks. Through supporting OLAP queries in any time range, users can obtain summarized information of the network in the time range of interest, which cannot be derived by using traditional static graph OLAP techniques. We propose a segment-tree based indexing technique to speed up the OLAP queries, and also develop an index-updating technique to maintain the index when the temporal multidimensional network evolves over time. In addition, we also propose a novel concept called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {similarity{\kern3.0pt}of{\kern3.0pt}snapshots}$</tex-math></inline-formula> which shows a strong correlation with the efficiency of indexing technique and can provide a good reference on the necessity of building the index. The results of extensive experiments on two large real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

Performance Analysis of Heuristic Miner and Genetics Algorithm in Process Cube: a Case Study

Databases that are processed in the form of Online Analytical Processing (OLAP) can solve large query loads that cannot be resolved by transactional databases. OLAP systems are based on a multidimensional model commonly called a cube. In this study, OLAP techniques are applied in process mining, a method for bridging analysis based on business process models with database analysis. Like data mining, process mining produces process models by implementing the algorithms. This study implements the heuristic miner algorithm compared with genetic algorithms. The selection of these two algorithms is due to the characteristics to be able to model the event log correctly and can handle the control-flow. The capability in handling control-flow including the ability to detect hidden task, looping, duplicate task, detecting implicit/explicit concurrency, non-free-choice, the ability to mine and exploiting time, overcoming noise, and overcome incompleteness. The results of conformance checking on the heuristic miner algorithm for all data, fitness values, position, and structure are 1, 0.495, and 1, while the results of the genetic algorithm are 0.977, 0.706 and 1. Both algorithms have good ability in modeling processes and have high accuracy. The results of the F-score calculation on the heuristic miner algorithm for all data is 0.622, while the result in the genetic algorithm is 0.820. It indicates that genetic algorithms have better performance in modeling event logs based on process cube.

Building Data Warehouse for Diseases Registry: First Step for Clinical Data Warehouse

Almost all the Decision Makers in medical path need a good platform which providing analytical results to use themin order to support their decisions.Due to vast amount of patient's records in Electronic Health Record(EHR) rather than Electronic Medical Records(EMR),Medical data can be used to get valuable information to support decisions of specialists in clinical path.Clinicaldata warehouse can combine different sources of clinicaldata into single repository and use it to produce analytical information to support decisions. Since Online Analytical Processing (OLAP) can be used in analyzing data and find the relationship between many factors and provide good view of data from many points of view so using data warehousing and OLAP techniques is the good choice for helping professionals and analysts in finding their goals.We will use diseases registry data (Part from EMR) to design and implement data warehouse as the first step that could be used later by OLAP or Data Mining to support Clinical Decisions. In this work we combined different sources of diseases registry dataintosingle repository.

Prediction in OLAP Data Cubes

Online analytical processing (OLAP) provides tools to explore data cubes in order to extract the interesting information, it refers to techniques used to query, visualise and synthesise the multidimensional data. Nevertheless OLAP is limited on visualisation, structuring and exploring manually the data cubes. On the other side, data mining allows algorithms that offer automatic knowledge extraction, such as classification, explanation and prediction algorithms. However, OLAP is not capable of explaining and predicting events from existing data; therefore, it is possible to make a more efficient online analysis by coupling data mining and OLAP to allow the user to assist in this new task of knowledge extraction. In this paper, we will carry on within works achieved in this theme and we suggest to extend the abilities of OLAP to prediction (enhancing the OLAP abilities and techniques by introducing a predictive model based on a data mining algorithms). The model is calculated on the aggregated data, and prediction is done on detailed missing data. Our approach is based on regression trees and neural networks; it consists to predict facts having a missed measures value in the data cubes. The user will have in his disposition, a new platform called PredCube, that offers the possibility to query, visualise and synthesise the multidimensional data, and also to predict missing values in the data cube using three data mining methods, and evaluate the quality of the prediction by comparing the average error and the execution time given by each one.

Application of Data Warehouse in Real Life: State-of-the-art Survey from User Preferences’ Perspective

In recent years, due to increase in data complexity and manageability issues, data warehousing has attracted a great deal of interest in real life applications especially in business, finance, healthcare and industries. As the importance of retrieving the information from knowledge-base cannot be denied, data warehousing is all about making the information available for decision making. Data warehouse is accepted as the heart of the latest decision support systems. Due to the eagerness of data warehouse in real life, the need for the design and implementation of data warehouse in different applications is becoming crucial. Information from operational data sources are integrated by data warehousing into a central repository to start the process of analysis and mining of integrated information and primarily used in strategic decision making by means of online analytical processing techniques (OLAP). Despite the applications of data warehousing techniques in number of areas, there is no comprehensive literature review for it. This survey paper is an effort to present the applications of data warehouse in real life. It focuses to help the scholars knowing the analysis of data warehouse applications in number of domains. This survey provides applications, case studies and analysis of data warehouse used in various domains based on user preferences.

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.

Parallel online spatial and temporal aggregations on multi-core CPUs and many-core GPUs

With the increasing availability of locating and navigation technologies on portable wireless devices, huge amounts of location data are being captured at ever growing rates. Spatial and temporal aggregations in an Online Analytical Processing (OLAP) setting for the large-scale ubiquitous urban sensing data play an important role in understanding urban dynamics and facilitating decision making. Unfortunately, existing spatial, temporal and spatiotemporal OLAP techniques are mostly based on traditional computing frameworks, i.e., disk-resident systems on uniprocessors based on serial algorithms, which makes them incapable of handling large-scale data on parallel hardware architectures that have already been equipped with commodity computers. In this study, we report our designs, implementations and experiments on developing a data management platform and a set of parallel techniques to support high-performance online spatial and temporal aggregations on multi-core CPUs and many-core Graphics Processing Units (GPUs). Our experiment results show that we are able to spatially associate nearly 170 million taxi pickup location points with their nearest street segments among 147,011 candidates in about 5–25s on both an Nvidia Quadro 6000 GPU device and dual Intel Xeon E5405 quad-core CPUs when their Vector Processing Units (VPUs) are utilized for computing intensive tasks. After spatially associating points with road segments, spatial, temporal and spatiotemporal aggregations are reduced to relational aggregations and can be processed in the order of a fraction of a second on both GPUs and multi-core CPUs. In addition to demonstrating the feasibility of building a high-performance OLAP system for processing large-scale taxi trip data for real-time, interactive data explorations, our work also opens the paths to achieving even higher OLAP query efficiency for large-scale applications through integrating domain-specific data management platforms, novel parallel data structures and algorithm designs, and hardware architecture friendly implementations.

OLAP based Scaffolding to support Personalized Synchronous e-Learning

The advent of asynchronous web based learning systems has helped the learner in a self paced, personalized and flexible learning style. It can be even more useful with a supportive synchronous tutorial (question-answer) session. The challenge is to provide sufficient information to the instructor about the learner’s experience in that particular course at run time. Online analytical processing (OLAP) is a very useful technique in producing such run time information in the form of reports. In this paper we have designed an automated scaffolding technique to hold this vital information about the learner which we have obtained by OLAP techniques on the log data of the LMS users. We have also proposed an overall architecture of the scaffolding where this information can be easily accessed and used by the instructor in the synchronous tutorial session to make the system more adaptive.

Apply On-Line Analytical Processing (OLAP)With Data Mining For Clinical Decision Support

Medicine is a new direction in his mission is to prevent, diagnose and medicate diseases using OLAP with data mining. Are analyzed clinical data on patient population and the wide range of performance management of health care, unfortunately, are not converted to useful information for effective decisionmaking. Built OLAP and data mining techniques in the field of health care, and an easy to use decision support platform, which supports the decision-making process of caregivers and clinical managers. This paper presents a model for clinical decision support system which combines the strengths of both OLAP and data mining. It provides a knowledge rich environment that cannot be achieved by using OLAP or data mining alone.

Apply On-Line Analytical Processing (OLAP)With Data Mining For Clinical Decision Support

Medicine is a new direction in his mission is to prevent, diagnose and medicate diseases using OLAP with data mining. Are analyzed clinical data on patient population and the wide range of performance management of health care, unfortunately, are not converted to useful information for effective decision making. Built OLAP and data mining techniques in the field of health care, and an easy to use decision support platform, which supports the decision-making process of caregivers and clinical managers. This paper presents a model for clinical decision support system which combines the strengths of both OLAP and data mining. It provides a knowledge rich environment that cannot be achieved by using OLAP or data mining alone.

PubMine: An Ontology-Based Text Mining System for Deducing Relationships among Biological Entities

Background: Published manuscripts are the main source of biological knowledge. Since the manual examination is almost impossible due to the huge volume of literature data (approximately 19 million abstracts in PubMed), intelligent text mining systems are of great utility for knowledge discovery. However, most of current text mining tools have limited applicability because of i) providing abstract-based search rather than sentence-based search, ii) improper use or lack of ontology terms, iii) the design to be used for specific subjects, or iv) slow response time that hampers web services and real time applications. Results: We introduce an advanced text mining system called PubMine that supports intelligent knowledge discovery based on diverse bio-ontologies. PubMine improves query accuracy and flexibility with advanced search capabilities of fuzzy search, wildcard search, proximity search, range search, and the Boolean combinations. Furthermore, PubMine allows users to extract multi-dimensional relationships between genes, diseases, and chemical compounds by using OLAP (On-Line Analytical Processing) techniques. The HUGO gene symbols and the MeSH ontology for diseases, chemical compounds, and anatomy have been included in the current version of PubMine, which is freely available at http://pubmine.kobic.re.kr. Conclusions: PubMine is a unique bio-text mining system that provides flexible searches and analysis of biological entity relationships. We believe that PubMine would serve as a key bioinformatics utility due to its rapid response to enable web services for community and to the flexibility to accommodate general ontology.

Integrating Data Warehouses with Web Data: A Survey

This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML technologies that are currently being used to integrate, store, query and retrieve web data, and their application to DWs. The paper reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces the problem of dealing with semi-structured data in a DW. It studies Web data repositories, the design of multidimensional databases for XML data sources and the XML extensions of On-Line Analytical Processing techniques. The paper addresses the application of information retrieval technology in a DW to exploit text-rich documents collections. The authors hope that the paper will help to discover the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as, to identify open research lines.

Warehousing complex data from the web

Data warehousing and Online Analytical Processing (OLAP) technologies are now moving onto handling complex data that mostly originate from the web. However, integrating such data into a decision-support process requires their representation in a form processable by OLAP and/or data mining techniques. We present in this paper a complex data warehousing methodology that exploits eXtensible Markup Language (XML) as a pivot language. Our approach includes the integration of complex data in an ODS, in the form of XML documents; their dimensional modelling and storage in an XML data warehouse; and their analysis with combined OLAP and data mining techniques. We also address the crucial issue of performance in XML warehouses.

Models of multi-dimensional analysis for qualitative data and its application

Online analytical processing (OLAP) is one of the technologies that enable client applications to efficiently access data multi-dimensionally. This powerful tool helps users create new views of data, based on a rich array of ad hoc calculation functions. However, the complexity of queries required to support OLAP applications in the multi-dimensional model makes OLAP difficult to implement by simply using standard relational database technology in a static manner. Moreover, OLAP requires numerical data input. In contrast, qualitative data cannot be operated on using OLAP technique. This paper develops models of multi-dimensional analysis, based on traditional multi-dimensional techniques and OLAP techniques to analyze qualitative data dynamically. The models are able to discover the kernel knowledge from the current formulated knowledge. The proposed model is used to develop multi-dimensional algebra to facilitate operation in data warehouse.

Space Environment Information System for Mission Control Purposes - a Decision Support System based on an architecture for space weather services

The radiation environment encountered in space by satellites is extremely variable and depends mainly on the satellite position and space weather. Although models for the concerned processes are available, most of them only represent the average conditions and neglect the dynamics of the system. Accurate prediction of the radiation environment remains an unsolved problem. Space weather can cause manifold problems to spacecraft (S/C) components, such as degradation of sensors and solar arrays and changes in on-board memories by Single Event Upsets (SEU). The final effect is a degradation of the S/C overall performance and in extreme cases complete unavailability of services. When certain alarm conditions are reached, risk avoidance procedures may be invoked, e.g. switching off high voltages/biases/filters etc. and transition to protected operating modes. Once the detectors are off, there is just a rough estimation of when the conditions are safe again. Better prediction of radiation conditions and more accurate information could greatly improve these operations. Therefore it is necessary to monitor and predict the space weather effects and improve the space weather services. In this paper we present a system for correlation, monitoring and predicting of space weather data, which is being developed by UNINOVA (Institute for the Development of New Technologies, Caparica, Portugal) in the frame of the European Space Agency (ESA) project, Space Environment Information System for Mission Control Purposes (SEIS) project. This decision support system (DSS) provides Flight Control Teams with useful Space Weather information (past, current and future) to increase the ability to protect S/C components from hazardous events and therefore prolong the lifetime of satellites and their scientific return. The system design was inspired by traditional business oriented DSS, based on Data Warehousing storage techniques, which collect and integrate historical and real-time data from heterogeneous sources, e.g. Space Weather data providers, S/C Telemetry and Orbital data and historical SEU records. The services provided include exploration and correlation analysis of data by established OLAP (On Line Analytical Processing) techniques, and near real-time monitoring of S/C instruments susceptible to Space Weather conditions. The monitoring service will directly depend on a Knowledge Base (representation of the domain experts knowledge), allowing it to suggest appropriate recovery actions whenever possible S/C damaging conditions are identified. Additionally, the system will incorporate forecasting services of well-known and commonly used physical radiation models, as well as new prediction techniques based on nonlinear models such as Artificial Neural Networks. For more information, please refer to: http://www2.uninova.pt/ca3/en/project SEIS.htm I.D. is supported by FCT, Lisboa, Portugal under grant number SFRH/BPD/14628/2003.