OLAP Database Research Articles

A Simpler and Semantic Multidimensional Database Query Language to Facilitate Access to Information in Decision-making

OLAP and multidimensional database technology have contributed significantly to speed up and build confidence in the effectiveness of methodologies based on the use of management indicators in decision-making, industry, production, and services. Although there are a wide variety of tools related to the OLAP approach, many implementations are performed in relational database systems (R-OLAP). So, all interrogation actions are performed through queries that must be reinterpreted in the SQL language. This translation has several consequences because SQL language is based on a mixture of relational algebra and tuple relational calculus, which conceptually responds to the logic of the relational data model, very different from the needs of the multidimensional databases. This paper presents a multidimensional query language that allows expressing multidimensional queries directly over ROLAP databases. The implementation of the multidimensional query language will be done through a middleware that is responsible for mapping the queries, hiding the translation to a layer of software not noticeable to the end-user. Currently, progress has been made in the definition of a language where through a key statement, called aggregate, it is possible to execute the typical multidimensional operators which represent an important part of the most frequent operations in this type of database.

Application of the meta-modelling method in Business Intelligence

The article deals with the issue of conceptual modelling of the MOLAP database structures used in multidimensional data analysis for BI. Management information for decision-making purposes was collected in the MOLAP database and delivered to the user via the management cockpit. Building OLAP databases is discussed from the database designer point of view The meta model of the OLAP database was described in conceptual and logical terms. The work includes a critical analysis of the meta-model assumptions for the design of systems for multidimensional data analysis. A postulate to revise the classic meta-model was presented and a new type of relationship was proposed. The assumptions of the meta-model for modelling and simulating business decisions were presented, as well as the application of meta modeling to define business processes. Based on Gozinto graphs, the meta model of structural and technological developments was defined

AnalyticDB

With data explosion in scale and variety, OLAP databases play an increasingly important role in serving real-time analysis with low latency (e.g., hundreds of milliseconds), especially when incoming queries are complex and ad hoc in nature. Moreover, these systems are expected to provide high query concurrency and write throughput, and support queries over structured and complex data types (e.g., JSON, vector and texts). In this paper, we introduce AnalyticDB, a real-time OLAP database system developed at Alibaba. AnalyticDB maintains all-column indexes in an asynchronous manner with acceptable overhead, which provides low latency for complex ad-hoc queries. Its storage engine extends hybrid row-column layout for fast retrieval of both structured data and data of complex types. To handle large-scale data with high query concurrency and write throughput, AnalyticDB decouples read and write access paths. To further reduce query latency, novel storage-aware SQL optimizer and execution engine are developed to fully utilize the advantages of the underlying storage and indexes. AnalyticDB has been successfully deployed on Alibaba Cloud to serve numerous customers (both large and small). It is capable of holding 100 trillion rows of records, i.e., 10PB+ in size. At the same time, it is able to serve 10m+ writes and 100k+ queries per second, while completing complex queries within hundreds of milliseconds.

Choosing a cloud DBMS

As analytic (OLAP) applications move to the cloud, DBMSs have shifted from employing a pure shared-nothing design with locally attached storage to a hybrid design that combines the use of shared-storage (e.g., AWS S3) with the use of shared-nothing query execution mechanisms. This paper sheds light on the resulting tradeoffs, which have not been properly identified in previous work. To this end, it evaluates the TPC-H benchmark across a variety of DBMS offerings running in a cloud environment (AWS) on fast 10Gb+ networks, specifically database-as-a-service offerings (Redshift, Athena), query engines (Presto, Hive), and a traditional cloud agnostic OLAP database (Vertica). While these comparisons cannot be apples-to-apples in all cases due to cloud configuration restrictions, we nonetheless identify patterns and design choices that are advantageous. These include prioritizing low-cost object stores like S3 for data storage, using system agnostic yet still performant columnar formats like ORC that allow easy switching to other systems for different workloads, and making features that benefit subsequent runs like query precompilation and caching remote data to faster storage optional rather than required because they disadvantage ad hoc queries.

Hybrid Disk Drive Configuration on Database Server Virtualization

SSD is a revolutionary new storage technologies. Enterprise storage system using full SSD is still very expensive, while HDD is still widely used. This study discusses hybrid configuration storage in virtualized server database with benchmark against four hybrid storage configuration for four databases, ORACLE, SQL Server, MySQL and PostgreSQL on Windows Server virtualization. Benchmark using TPC-C and TPC-H to get the best performance of four configurations were tested. The results of this study indicate HDD storage configurations as visual disk drive OS and SSD as visual disk drives database get better performance as OLTP and OLAP database server compared with SSD as visual disk drive OS and HDD as a visual disk drive database. Based on the data research TPC-C, OLTP get best performance at HDD storage configurations as visual disk drive OS and SSD as a visual disk drives database and temporary files.

국가 정보보호 지수 산출을 위한 OLAP 데이터베이스 시스템의 구축

UN, OECD, ITU 등 국제기구들은 정기적으로 정보 정책을 수립하고 평가하는데 활용하기 위한 목적으로 정보화 지수를 발표한다. 정보화 지수는 국가의 정보 정책의 수행능력을 평가하고 미래에 진행할 정부의 정보화 프로젝트를 선택하기 위한 기준으로서 활용된다. 정보 시스템이 고도화됨에 따라 이와 더불어 정보보안 역시 그 중요성이 부각되고 있는 실정이고, 이에 따라 국가 정보보호 지수의 산출이 필요하게 되었다. 정보 보안 지수는 특정 그룹의 정보 보안의 특성을 가장 명확하게 표현하는 공식 수치 값이며, 이는 정부의 정보 보안 정책을 결정하는데 이용된다. 국가 정보보호 지수를 산출하는 데에는 현재 설문지 조사방법이 사용되고 있는데, 이 방법으로는 충분한 통계 자료를 확보하기 어려우며 그 자료의 신뢰성 또한 높지 않다는 문제점이 존재한다. 본 연구에서는 이를 해결하기 위해 기존의 설문지 조사방법이 아닌 개별 기업의 정보보호 수준에 관한 원시데이터를 정확하게 수집할 수 있는 새로운 방법을 제시하고, 다음으로 원시데이터를 근간으로 하여 머크로 데이터를 얻어 정책 의사결정을 할 수 있는 OLAP 데이터베이스 시스템을 구축하는 것에 목적을 두고 있다. 현 OLAP 체제에서는 모든 계층구조 스키마가 분배적으로 되는 경우에만 제대로 집계데이터를 구할 수 있기 때문에 본 연구에서는 사례의 비 분배적 계층구조 스키마를 분배적 스키마로 변환하여 국가 정보보호 지수를 산출하는 OLAP 데이터베이스 시스템을 구축하였다. 본 연구에서 제안하는 방법은 구현의 관점과 스키마 설계의 관점에서 볼 때에 유용한 방법이 되리라 생각한다. UN, OECD, ITU and other international organizations regularly announce ISI (Information Society Index) to utilize in establishing and evaluating information policies. ISI is utilized as important data for countries to evaluate their information policy performance and select future projects. As the advancement of information systems, the importance of information security has been emerged. Accordingly, NISI (National Information Security Index) has been required. NISI number is the most clearly figure to express the characteristics of a particular group's information security. It can be utilized in determining information security policies. Currently, questionnaire method has been used to calculate NISI number. But there is an absolute lack of statistical data, and the reliability of surveyed statistical data is problematic. The objective of this paper is to show how to collect precise micro data of each company's information security index numbers, and to develop an OLAP database system which calculating NISI numbers by using those micro data. In this process of the survey, we presented the technique to collect the data more systematically, and to analyze the data without using questionnaire method. OLAP architecture performs only well on the facts that are summarizable along each dimension, where all hierarchy schemas are distributive. Therefore we transformed the non-distributive hierarchy schema into the distributive hierarchy schema to implement OLAP database system. It is thought that this approach will be useful one from an implementation and schema design point of view.

Reduced representations of Emerging Cubes for OLAP database mining

In this paper, we investigate reduced representations for the Emerging Cube. We use the borders, classical in data mining, for the Emerging Cube. These borders can support classification tasks to know whether a trend is emerging or not. However, the borders do not make possible to retrieve the measure values. This is why we introduce two new and reduced representations without measure loss: the L-Emerging Closed Cube and Emerging Quotient Cube. We state the relationship between the introduced representations. Experiments performed on various data sets are intended to measure the size of the three reduced representations.

Treatment- and diagnosis-related benchmarking of population groups in Austria. A dynamic information tool

In the past, a nationwide analysis of hospital data has not been realisable for Austrian sickness funds due to the federal character of the Austrian healthcare system. However, decision makers in the Social Security and Health Care Platforms need this nationwide analysis to gain the information and knowledge for governing and planning the healthcare system. In order to supply support for this decision-making process, the Main Association of the Austrian Social Security Institutions – an umbrella organisation of all Social Security Funds – started to develop a software-based benchmarking system that allows a comparative analysis of the hospitalization rate, as well as the related diagnoses and medical treatments for all Austrian regional population groups. For the analysis, we use the hospital data, which is being reported by the public hospitals to the Ministry of Health, and provide the results via an OLAP database and web-based standard reports on the Social Security Intranet.

OLAP, relational, and multidimensional database systems

article Free Access Share on OLAP, relational, and multidimensional database systems Author: George Colliat Arbor Software Corp., Sunnyvale, CA Arbor Software Corp., Sunnyvale, CAView Profile Authors Info & Claims ACM SIGMOD RecordVolume 25Issue 3Sept. 1996 pp 64–69https://doi.org/10.1145/234889.234901Online:01 September 1996Publication History 151citation1,797DownloadsMetricsTotal Citations151Total Downloads1,797Last 12 Months252Last 6 weeks31 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF