scholarly journals Knowledge Graph OLAP

Semantic Web ◽  
2020 ◽  
pp. 1-35
Author(s):  
Christoph G. Schuetz ◽  
Loris Bozzato ◽  
Bernd Neumayr ◽  
Michael Schrefl ◽  
Luciano Serafini
Keyword(s):  
Data Analysis ◽  
Real World ◽  
Multidimensional Data ◽  
Knowledge Graph ◽  
Analytical Processing ◽  
Hierarchical Nature ◽  
High Level ◽  
Multidimensional Models ◽  
Query Operations ◽  
Selection Of

A knowledge graph (KG) represents real-world entities and their relationships. The represented knowledge is often context-dependent, leading to the construction of contextualized KGs. The multidimensional and hierarchical nature of context invites comparison with the OLAP cube model from multidimensional data analysis. Traditional systems for online analytical processing (OLAP) employ multidimensional models to represent numeric values for further analysis using dedicated query operations. In this paper, along with an adaptation of the OLAP cube model for KGs, we introduce an adaptation of the traditional OLAP query operations for the purposes of performing analysis over KGs. In particular, we decompose the roll-up operation from traditional OLAP into a merge and an abstraction operation. The merge operation corresponds to the selection of knowledge from different contexts whereas abstraction replaces entities with more general entities. The result of such a query is a more abstract, high-level view – a management summary – of the knowledge.

Empowering the OLAP Technology to Support Complex Dimension Hierarchies

2011 ◽  
pp. 450-470
Author(s):  
Svetlana Mansmann
Keyword(s):  
Data Analysis ◽  
Real World ◽  
Multidimensional Data ◽  
Complex Data ◽  
Two Phase ◽  
Academic Administration ◽  
Complex Dimension ◽  
On Line ◽  
Analytical Processing ◽  
Uniform Manner

Comprehensive data analysis has become indispensable in a variety of domains. OLAP (On-Line Analytical Processing) systems tend to perform poorly or even fail when applied to complex data scenarios. The restriction of the underlying multidimensional data model to admit only homogeneous and balanced dimension hierarchies is too rigid for many real-world applications and, therefore, has to be overcome in order to provide adequate OLAP support. We present a framework for classifying and modeling complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. The properties of various hierarchy types are formalized and a two-phase normalization approach is proposed: heterogeneous dimensions are reshaped into a set of well-behaved homogeneous subdimensions, followed by the enforcement of summarizability in each dimension’s data hierarchy. Mapping the data to a visual data browser relies solely on metadata, which captures the properties of facts, dimensions, and relationships within the dimensions. The navigation is schema-based, that is, users interact with dimensional levels with ondemand data display. The power of our approach is exemplified using a real-world study from the domain of academic administration.

Schema Evolution Models and Languages for Multidimensional Data Warehouses

2009 ◽  
pp. 119-128
Author(s):  
Edgard Benítez-Guerrero ◽  
Ericka-Janet Rechy-Ramírez
Keyword(s):  
Data Analysis ◽  
Data Warehouse ◽  
Historical Data ◽  
Multidimensional Systems ◽  
Multidimensional Data ◽  
Schema Evolution ◽  
Time Dimension ◽  
On Line ◽  
Analytical Processing ◽  
Factual Data

A Data Warehouse (DW) is a collection of historical data, built by gathering and integrating data from several sources, which supports decisionmaking processes (Inmon, 1992). On-Line Analytical Processing (OLAP) applications provide users with a multidimensional view of the DW and the tools to manipulate it (Codd, 1993). In this view, a DW is seen as a set of dimensions and cubes (Torlone, 2003). A dimension represents a business perspective under which data analysis is performed and organized in a hierarchy of levels that correspond to different ways to group its elements (e.g., the Time dimension is organized as a hierarchy involving days at the lower level and months and years at higher levels). A cube represents factual data on which the analysis is focused and associates measures (e.g., in a store chain, a measure is the quantity of products sold) with coordinates defined over a set of dimension levels (e.g., product, store, and day of sale). Interrogation is then aimed at aggregating measures at various levels. DWs are often implemented using multidimensional or relational DBMSs. Multidimensional systems directly support the multidimensional data model, while a relational implementation typically employs star schemas(or variations thereof), where a fact table containing the measures references a set of dimension tables.

GENERAL ASSESSMENT OF THE PROSPECTS FOR THE APPLICATION OF THE BIG DATA PARADIGM IN SOCIO-ECONOMIC SYSTEMS

2021 ◽  
pp. 47-52
Author(s):  
V. A. Konovalov
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Multidimensional Data ◽  
Economic Systems ◽  
Mathematical Methods ◽  
Data Synthesis ◽  
Analytical Processing ◽  
Economic Nature ◽  
The Impact ◽  
Scientific Ideas

The paper assesses the prospects for the application of the big data paradigm in socio-economic systems through the analysis of factors that distinguish it from the well-known scientific ideas of data synthesis and decomposition. The idea of extracting knowledge directly from big data is analyzed. The article compares approaches to extracting knowledge from big data: algebraic and multidimensional data analysis used in OLAP-systems (OnLine Analytical Processing). An intermediate conclusion is made about the advisability of dividing systems for working with big data into two main classes: automatic and non-automatic. To assess the result of extracting knowledge from big data, it is proposed to use well-known scientific criteria: reliability and efficiency. It is proposed to consider two components of reliability: methodical and instrumental. The main goals of knowledge extraction in socio-economic systems are highlighted: forecasting and support for making management decisions. The factors that distinguish big data are analyzed: volume, variety, velocity, as applied to the study of socio-economic systems. The expediency of introducing a universe into systems for processing big data, which provides a description of the variety of big data and source protocols, is analyzed. The impact of the properties of sample populations from big data: incompleteness, heterogeneity, and non-representativeness, the choice of mathematical methods for processing big data is analyzed. The conclusion is made about the need for a systemic, comprehensive, cautious approach to the development of fundamental decisions of a socio-economic nature when using the big data paradigm in the study of individual socio-economic subsystems.

Evolution of Data Cube Computational Approaches

2011 ◽  
pp. 469-476
Author(s):  
Rebecca Boon-Noi Tan
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Support System ◽  
Database Systems ◽  
Data Cube ◽  
Multidimensional Data ◽  
Multidimensional Data Analysis ◽  
Computational Approaches ◽  
Simultaneous Group ◽  
Analytical Processing

Aggregation is a commonly used operation in decision support database systems. Users of decision support queries are interested in identifying trends rather than looking at individual records in isolation. Decision support system (DSS) queries consequently make heavy use of aggregations, and the ability to simultaneously aggregate across many sets of dimensions (in SQL terms, this translates to many simultaneous group-bys) is crucial for Online Analytical Processing (OLAP) or multidimensional data analysis applications (Datta, VanderMeer, & Ramamritham, 2002; Dehne, Eavis, Hambrusch, & Rau-Chaplin, 2002; Elmasri & Navathe, 2004; Silberschatz, Korth & Sudarshan, 2002).

Empowering the OLAP Technology to Support Complex Dimension Hierarchies

2008 ◽  
pp. 2164-2184
Author(s):  
Svetlana Mansmann ◽  
Marc H. Scholl
Keyword(s):  
Real World ◽  
Multidimensional Data ◽  
Complex Data ◽  
Two Phase ◽  
Academic Administration ◽  
On Demand ◽  
Complex Dimension ◽  
On Line ◽  
Analytical Processing ◽  
Uniform Manner

Comprehensive data analysis has become indispensable in a variety of domains. OLAP (On-Line Analytical Processing) systems tend to perform poorly or even fail when applied to complex data scenarios. The restriction of the underlying multidimensional data model to admit only homogeneous and balanced dimension hierarchies is too rigid for many real-world applications and, therefore, has to be overcome in order to provide adequate OLAP support. We present a framework for classifying and modeling complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. The properties of various hierarchy types are formalized and a two-phase normalization approach is proposed: heterogeneous dimensions are reshaped into a set of wellbehaved homogeneous subdimensions, followed by the enforcement of summarizability in each dimension’s data hierarchy. Mapping the data to a visual data browser relies solely on metadata, which captures the properties of facts, dimensions, and relationships within the dimensions. The navigation is schema-based, that is, users interact with dimensional levels with on-demand data display. The power of our approach is exemplified using a real-world study from the domain of academic administration.

Failure Analysis Enhancement by Incorporating a Compact Scan Diagnosis System

2014 ◽  
Author(s):  
Rommel Estores ◽  
Pascal Vercruysse ◽  
Karl Villareal ◽  
Eric Barbian ◽  
Ralph Sanchez ◽  
...  
Keyword(s):  
Data Analysis ◽  
Failure Analysis ◽  
Real Analysis ◽  
Failure Data ◽  
Analysis Methodology ◽  
Efficiency And Effectiveness ◽  
Scan Chain ◽  
On Chip ◽  
Analysis Environment ◽  
Selection Of

Abstract The failure analysis community working on highly integrated mixed signal circuitry is entering an era where simultaneously System-On-Chip technologies, denser metallization schemes, on-chip dissipation techniques and intelligent packages are being introduced. These innovations bring a great deal of defect accessibility challenges to the failure analyst. To contend in this era while aiming for higher efficiency and effectiveness, the failure analysis environment must undergo a disruptive evolution. The success or failure of an analysis will be determined by the careful selection of tools, data and techniques in the applied analysis flow. A comprehensive approach is required where hardware, software, data analysis, traditional FA techniques and expertise are complementary combined [1]. This document demonstrates this through the incorporation of advanced scan diagnosis methods in the overall analysis flow for digital functionality failures and supporting the enhanced failure analysis methodology. For the testing and diagnosis of the presented cases, compact but powerful scan test FA Lab hardware with its diagnosis software was used [2]. It can therefore easily be combined with the traditional FA techniques to provide stimulus for dynamic fault localizations [3]. The system combines scan chain information, failure data and layout information into one viewing environment which provides real analysis power for the failure analyst. Comprehensive data analysis is performed to identify failing cells/nets, provide a better overview of the failure and the interactions to isolate the fault further to a smaller area, or to analyze subtle behavior patterns to find and rationalize possible faults that are otherwise not detected. Three sample cases will be discussed in this document to demonstrate specific strengths and advantages of this enhanced FA methodology.

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

THE EFFECT OF ONLINE VIDEO BLOG ON STUDENTS’ ACHIEVEMENT OF PASSIVE VOICE IN 4J OPERATION KENDARI

2020 ◽  
Vol 5 (2) ◽  
pp. 125
Author(s):  
Raden Alifian Setiawan ◽  
Hanna Hanna ◽  
Alberth Alberth
Keyword(s):  
Data Analysis ◽  
Real World ◽  
T Test ◽  
Test Design ◽  
Online Video ◽  
Test Results ◽  
Multiple Choice Questions ◽  
Passive Voice ◽  
Paired Sample ◽  
Post Test

The use of videos in education makes it possible to overcome practical real-world constraints and explore far greater possibilities provided by digital spaces, especially for the video uploaded in online platform such as blog. This study examines whether online video blog as media have a significant effect on students’ achievement of passive voice. It used pre-experimental (one group pre-test and post-test) design. The samples of this study were 10 students at 4J Operation. A pre-test and post-test were conducted by using multiple choice questions as the instruments. Data analysis was through paired-sample t-test. Results showed that there was an increase in mean score of pre-test (49,1) and post-test (63,5). Data from Paired Sample t-test showed that Sig. (2-tailed) was 0.000 which was smaller than .05 which means that there was significance difference in mean score after employing treatment.

SBA MCQs for the MRCS Part A

2012 ◽  
Author(s):  
Sri G. Thrumurthy ◽  
Tania Samantha De Silva ◽  
Zia Moinuddin ◽  
Stuart Enoch
Keyword(s):  
Clinical Case ◽  
Multiple Choice ◽  
Surgical Anatomy ◽  
Surgical Patients ◽  
Multiple Choice Questions ◽  
Basic Sciences ◽  
Self Assessment ◽  
High Level ◽  
Factual Recall ◽  
Selection Of

Specifically designed to help candidates revise for the MRCS exam, this book features 350 Single Best Answer multiple choice questions, covering the whole syllabus. Containing everything candidates need to pass the MRCS Part A SBA section of the exam, it focuses intensively on the application of basic sciences (applied surgical anatomy, physiology, and pathology) to the management of surgical patients. The high level of detail included within the questions and their explanations allows effective self-assessment of knowledge and quick identification of key areas requiring further attention. Varying approaches to Single Best Answer multiple choice questions are used, giving effective exam practice and guidance through revision and exam technique. This includes clinical case questions, 'positively-worded' questions, requiring selection of the most appropriate of relatively correct answers; 'two-step' or 'double-jump' questions, requiring several cognitive steps to arrive at the correct answer; as well as 'factual recall' questions, prompting basic recall of facts.

Sign in / Sign up

Export Citation Format

Share Document