Fact Constellation Research Articles

Data Warehouse Design for Firefighters Operational at the DKI Jakarta Fire Department

This paper proposed two models of data warehouse schema for the fire department of DKI Jakarta, where the 1st model contains six tables consisting of 3 fact and 3-dimensional tables, and the 2nd model only contains three fact tables. The 2nd model denormalises the 1st model, where the number of tables is less than the 1st model, where at the end of the day, the 2nd model will reduce the join table process, which increases the SQL performances. These two models have been recognised as fact constellation schema with more than one fact table and sharing dimension and sub-dimension tables. The database resources were collected from http://data.jakarta.go.id under the Fire and Rescue Service Agency. Those two data warehouse schema models were developed based on a report sector list, a report on Hydrants list, and vehicle register reports. This paper proposes to support Automatic Identification Systems (AIS) research, particularly implementing the data warehouse concept.

ANALYSIS AND DESIGN OF CONCEPTUAL MODELS OF DATA WAREHOUSE IN ARISTY WEDDING ORGANIZER

Wedding Organizer is a business that specializes in services that specifically help the bride and groom and families in the planning and supervision of the implementation of a series of events into a wedding party. The necessity of designing a conceptual model of data warehouse on a wedding organizer devoted to designing the model and the data flow that existed at the wedding organizer. The data warehouse is a collection of data from various sources placed together in a safae place for querying and reporting processes. The conceptual model of the data warehouse is a logic design that represents the data. Data warehouse design method used in the study is the dimensional data modeling Powell. The study resulted in the design of data into multiple tables then the tables is determined which inculeds the dimension tables and fact. The identification process is intended to design a star schema, snowflake schemas, and the fact constellation schema of existing data on the wedding organizer CV . Aristy so that in the future can be created a wedding organizer information system for CV. Aristy

A multidimensional data warehouse design to combat the health pandemics

The Covid-19 pandemic has brought about a new lifestyle for across the globe. Throughout this period, the use of holistic methods has become indispensable to deal with the enormous amount of data in this regard. It appears that the simplest way to tackle this issue is to spread the digitalization efforts concerning all data-based applications. Given the significance of pandemic data management, it is essential to have a data warehouse that collects, associates, and communicates these data. Containing a significant volume of structured data, warehousing can provide the necessary foundation for data mining and the development of analytical tools. To this end, the present paper proposes a data warehouse for combatting and managing pandemics, with the possibility to be enhanced for other personal or public health-related initiatives. In this research, the bottom-up data warehouse building methodology is used to construct a warehouse. A fact constellation schema model is utilized to accommodate the information ranging from citizen demographics to physician-prescribed drugs and laboratory tests. Sample queries are executed based on the proposed data warehouse for different purposes, and desired query results are obtained within proper response times. The proposed data warehouse contributes to countrywide implementation of pandemic practices and illuminates research on faster, less expensive, and safer management of citywide, nationwide, or worldwide health emergencies within a robust technical framework by governments.

Designing a Data Warehouse for Earthquake Risk Assessment of Buildings: A Case Study for Healthcare Facilities

Since earthquake is one of the most dangerous natural phenomena, it is necessary to be prepared for the negative consequences of the earthquake in advance. It is very important that healthcare facilities must continue to provide service during and after an earthquake. Therefore, this study focuses on designing a data warehouse model for the earthquake risk assessment of healthcare facilities which are needed much more than other public buildings physically. The proposed design utilizes a fact constellation schema model and take a public legislation containing principles regarding identification of risky buildings. This solution can provide a repository for data regarding earthquake risk assessment from different operational systems and play a key role in supporting critical decision-making process.

Efficiency Analysis of Data Warehouse Implementation for Forestry Industry Reports

As one of the environmentally sensitive areas, the forestry industry required fast and accurate data processing for decision making, and data warehouses could be used to meet those needs. Decision-makers need aggregate, historical, and multi-dimensional data. Provided a data warehouse to present aggregate data could improve efficiency in presenting data. In this study, the data warehouse was built using the fact constellation scheme and the ETL process using query commands in MySQL. The level of efficiency was measured by comparing the number of tables, record length, number of records, and total bytes needed for each report. The efficiency of the report presentation was also compared with OLTP data and from the data warehouse. The test results in data management obtained an efficiency level of 56% for the number of tables, 145% for the length of records, 15,833% for the number of records, and 48,846% for the total bytes. On average, the level of efficiency in data management was 15,720%, while the efficiency of report presentation speed was 852%. This study showed that the use of data warehouses was very efficient in managing aggregate data for the forestry industry.

Electronic farming records – A framework for normalising agronomic knowledge discovery

Nowadays, agriculture generates huge data volumes through different sources. The analysis of this big data enables extraction and deduction of useful knowledge that can be exploited at various levels of the farming process and by different actors (farmers, companies, and agronomists). Nevertheless, to analyse this data effectually, various data sources need to be standardized and integrated into a unified dataset. In this paper, we propose an electronic farming record model using a fact constellation schema that is flexible enough to incorporate various farming datasets and big data models. We also apply some analysis techniques to extract knowledge with the view to optimise crop yield, reduce cost and pesticide resistance, and protect the environment. This can be done by finding suitable quantities of soil properties (texture and pH), soil nutrients, seed rate, herbicides, insecticides, fungicides and adjuvants, which are individually assessed on the most popular crops in Europe. Through experimentation, we show that our models are efficient and very promising.

What's a Nice Company like Goldman Sachs Doing in the Supreme Court?

This essay considers the issues raised in the latest securities fraud class action to reach the Supreme Court – Goldman Sachs v. Arkansas Teacher Retirement System – and finds that the claims asserted therein against Goldman Sachs on behalf of open-market buyers of its common stock are claims that should have been asserted on behalf of Goldman Sachs (by means of a derivative action) and against the individuals who caused the losses at issue. The losses suffered by individual buyers of Goldman Sachs stock during the extraordinarily long forty-month alleged fraud period are minimal if they exist at all. Moreover, the law is quite clear that claims on behalf of the company arising from the same constellation of facts should take precedence over any claims on behalf of individual buyers. Yet the practice that has evolved is the opposite: Class claims take priority and company claims are settled for non-monetary governance reforms of dubious value rather than for real money. The forces that have led to this classic example of market failure are both fascinating and sinister. But the bottom line is that ordinary investors – such as investors in well-diversified mutual funds and index funds – end up losing far more than they gain from class actions. Indeed, index fund investors effectively pay out about twenty dollars for every dollar they recover. Thus, the best hope for reforming the system is for index funds to step up and intervene to assert the interests of diversified investors in favor of litigating such claims as derivative actions rather than as class actions.

Building a Lung and Ovarian Cancer Data Warehouse.

ObjectivesDespite the collection of vast amounts of data by the healthcare sector, effective decision-making in medical practice is still challenging. Data warehousing technology can be applied for the collection and management of clinical data from various sources to provide meaningful insights for physicians and administrators. Cancer data are extremely complicated and massive; hence, a clinical data warehouse system can provide insights into prevention, diagnosis and treatment processes through the use of online analytical processing tools for the analysis of multi-dimensional data at different granularity levels.MethodsIn this study, a clinical data warehouse was developed for lung cancer data, which were kindly provided by the United States National Cancer Institute. Lung and ovarian cancer data were imported in specific formats and cleaned to remove errors and redundancies. SQL server integration services (SSIS) were used for the extract-transform-load (ETL) process.ResultsThe design of the clinical data warehouse responds efficiently to all types of queries by adopting the fact constellation schema model. Various online analytical processing queries can be expressed using the proposed approach.ConclusionsThis model succeeded in responding to complex queries, and the analysis of data is facilitated by using online analytical processing cubes and viewing multilevel data details.

MATERIALIZED VIEWS QUANTUM OPTIMIZED PICKING for INDEPENDENT DATA MARTS QUALITY

Particular and timely unified information along with quick and effective query response times is the basic fundamental requirement for the success of any collection of independent data marts (data warehouse) which forms Fact Constellation Schema or Galaxy Schema. Because of the materialized view storage area, the materialization of all views is practically impossible thus suitable materialized views (MVs) picking is one of the intelligent decisions in designing a Fact Constellation Schema to get optimal efficiency. This study presents a framework for picking best-materialized view using Quantum Particle Swarm Optimization (QPSO) algorithm where it is one of the stochastic algorithm in order to achieve the effective combination of good query response time, low query handling cost and low view maintenance cost. The results reveals that the proposed method for picking best-materialized view using QPSO algorithm is better than other techniques via computing the ratio of query response time and compare it to the response time of the same queries on the materialized views. Ratio of implementing the query on the base table takes five times more time than the query implementation on the materialized views. Where the response time of queries through MVs access were found 0.084 seconds while by direct access queries were found 0.422 seconds. This outlines that the performance of query through materialized views access is 402.38% better than those directly access via data warehouse-logical.

Data warehouse model for population registration in Kerambitan village Tabanan Regency

Kerambitan village is one of the 15 villages in Kerambitan sub-district, Tabanan regency, Kerambitan village has 7 service hamlets. Kerambitan village population has more than 3,338 inhabitants consisting of 1,700 men and 1,638 women with a sex ratio of 103.79. . To realize good governance, it needs to be recorded well so that the information can be utilized by related agencies which can be used for receiving assistance.
 During this time the process of recording population events such as recording the receipt of assistance is done manually where the information is done in tabular form so it does not clearly describe the respective information groups.
 Data warehouse (DW or DWH), also known as enterprise data warehouse (EDW), is a system used for reporting and analyzing data, and is considered a core component of business intelligence, The purpose of building a data warehouse is to provide a system that allows the right data to reach the right end user at the right time. Thus, the main purpose of implementing this data warehouse system is to provide relevant and timely information in an easily understood format so that service decisions to the public can be made more efficiently and effectively.
 the purpose of this research is to form a data warehouse model scheme of population records owned by kerambitan village government and through modeling this data warehouse also provides reliable information from a collection of data, in addition this research also retrieves some information from the data warehouse in build based on kerambitan village population records
 in this study I chose to use the fact constellation scheme because there are fact tables that are interconnected with dimension tables and also fact tables that are related to other fact tables.
 The design model of the data warehouse is designed based on 3 excel table files, each of which has 1 table from the Government of Kerambitan Villages. In this case, 3 reports are proposed to be made based on the query results from the data warehouse.
 Through the application of a data warehouse that was formed, the executive or government can conduct analysis of the reports generated based on various dimensions that exist.

Model data warehouse untuk Operasional petugas pemadam kebakaran Pada dinas pemadam kebakaran provinsi DKI Jakarta

In this paper we design 2 data warehouse design models for the DKI Jakarta fire department. The first data warehouse design model is categorized as a fact constellation schema, where there are 3 fact tables, 2 dimensional tables and 1 sub dimension table, while the second data warehouse design model is a fact constellation schema which consists of only 3 fact tables as well. Both of these data warehouse models are displayed with the intention that the implementation of the data warehouse based on the fire extinguisher data will look different and that will certainly be faster by using the 2nd model which is categorized as an non normal data warehouse model which only consists of 3 fact tables which the lack of a join process in the fact table and the dimensions of the data warehouse will speed up the query process in building the data warehouse report. The design model of the data warehouse is designed based on 4 excel table files, each of which has 1 table downloaded from the DKI Jakarta fire department's website on the website data.jakarta.go.id.

Design Framework for a Data Mart in the Neonatal Intensive Care Unit.

Neonates who are critically ill are cared for in a neonatal intensive care unit (NICU) for continuous monitoring of their conditions. Physiological parameters such as heart rate, respiratory wave form, blood oxygen saturation, and body temperature are constantly monitored in the NICU. However, NICUs are not always equipped with a computer system for analyzing such data, identifying critical events, and providing decision support for a neonatologist. Therefore, a specialized computer system, commonly known as a data mart, should be developed for the NICU. An architectural framework for the design and development of an automated system for data collection, storage, and analysis for the NICU is proposed in this paper. Our study also deals with the implementation of advanced transformation functions such as fuzzy grouping and fuzzy lookup for data preparation and preprocessing. Furthermore, based on Kimball's dimensional modeling, a data gathering and accumulation system, fact constellation schema (galaxy schema) has been built with previously identified neonatal processes. Finally, an information delivery component has been proposed, wherein the neonatal data can be both analyzed with different data mining algorithms and visualized with various metrics. Our pioneering work demonstrates methods that streamline the process of data collection, data storage, analysis, and decision-making, which in turn increases efficiency in the NICU and saves lives. Our study, presented in this paper, describes the design and development process and discusses its utility in the NICU. Our results indicate that data mart is best suited for effective decision-making in the NICU.

ABOUT DATA MODELING SUBJECT DOMAINS PRACTICE-ORIENTED DIRECTION FOR UNIVERSAL SYSTEM OF STORAGE AND PROCESSING DATA

This article describes data modeling for practice-oriented subject domains they are basis of general data model for data warehouse creation. Describes short subject domains characteristic relationship to different types of any human activities at the current time. Offered appropriate data models, considered relationship between them as data processing and data warehouse creation, which can be built on information data storage technology and which has some characteristics as extensible complex subject domain, data integration, which get from any data sources, data time invariance with required temporal marks, relatively high data stability, search necessary compromises in data redundancy, system blocks modularity, flexibility and extensibility of architecture, high requirements to data storage security. It’s proposed general approach of data collection and data storage, appropriate data models, in the future, will integrate in one database scheme and create generalized scheme of data warehouse as type «constellation of facts». For getting of data models applies structural methodology and consider general principles of conceptual design. Using complex system, which can work with some information sources and represent data in convenient view for users will in-demand for analysis data selected subject domains and determination of possible relationships.

PROTOTIPE INTEGRASI DATA MORBIDITAS PASIEN PUSKESMAS KEDALAM DATA WAREHOUSE DI DINAS KESEHATAN KABUTEN BANTUL

Bantul District Health Office (DHO Bantul) is one of the agencies that currently experiencing problems to obtain accurate and current health information. Reports to be made routinely by puskesmas further recapitulated in Bantul Health Office are reports on outpatient morbidity such as the report of Integrated Disease Surveillance (STP), disease report by type, and others.From result of analysis and design of data warehouse based on fact constellation schema which include dimension: time, patient, age group, disease and puskesmas, furthermore can be analyzed further for the purpose of decision making using data mining. Furthermore, it can also be used to analyze patient data from various dimensions (time, patient, age group, illness and puskesmas), and analyze the growth of patient number from each period of time which is useful for the management of DHO Bantul. Successfully built prototype data integration morbidity of outpatient Puskesmas. To ease the burden of the surveillance officer to make a report to DHO Bantul has made the application of Integrated Disease Surveillance (STP). While some types of reports are needed every periodic can be simulated using instaview or pentaho report.

Spatially enabled emergency event analysis using a multi-level association rule mining method

Spatial attributes are important factors affecting the entire process of emergency management. Despite the importance of spatial attributes in emergency rescue, this subject has not been subjected to thorough examination in previous literature. This paper presents a new method that incorporates spatial predicates describing the spatial relationships between emergency locations and surrounding objects into emergency event analysis. More specifically, we developed three mechanisms to achieve spatially enabled emergency analysis. First, a novel fact constellation schemas that integrate the spatial predicates and non-spatial data of emergency events is created for multi-level analysis. Second, a filtering algorithm is designed to remove redundant predicates from the huge amount of spatial predicates for data association rule mining. Third, by incorporating the filtering method, a multi-level spatial data association algorithm was proposed to realize the analytical function for emergency events. Experiments were conducted, and the results validate the effectiveness of our method.

Integrating XML Data Into Multiple Rolap Data Warehouse Schemas

Data Warehouse is one of the most common ways for analyzing large data for decision based system. These data are often sourced from online transactional system. The transactional data are represented in different formats. XML is one of the worldwide standards to represent data in web based system. Numbers of organizations use XML for e-commerce and internet based applications. Integration of XML and data warehouse for the innovation of business logic and to enhance decision making has therefore emerged as a demanding area of research interest. This paper focuses on integrating XML data based on multiple related XML schemas, to an equivalent data warehouse schemas based on relational online analytical processing (ROLAP). This work bears a high relevance towards standardizing of the ETL phase (Extraction, Transformation, and Loading) of the OLAP projects. The novelty of the work is that more than one data warehouse schemas could be identified from a single related XML schema and each of them could be categorized as star schema or snowflake schema. Moreover if the individual schemas are found to be related according to the analysis, fact constellation could be identified. A new data structure, Schema Graph has been proposed in the process.

SAMSTARplus: An Automatic Tool for Generating Multi- Dimensional Schemas from an Entity-Relationship Diagram

This paper presents a tool that automatically generates multidimensional schemas for data warehouses from OLTP entity-relationship diagrams (ERDs). Based on user’s input parameters, it generates star schemas, snowflake schemas, or a fact constellation schema by taking advantage of only structural information of input ERDs. Hence, SAMSTARplus can help users reduce efforts for designing data warehouses and aids decision making.

Algebraic and Graphic Languages for OLAP Manipulations

This article deals with multidimensional analyses. Analyzed data are designed according to a conceptual model as a constellation of facts and dimensions, which are composed of multi-hierarchies. This model supports a query algebra defining a minimal core of operators, which produce multidimensional tables for displaying analyzed data. This user-oriented algebra supports complex analyses through advanced operators and binary operators. A graphical language, based on this algebra, is also provided to ease the specification of multidimensional queries. These graphical manipulations are expressed from a constellation schema and they produce multidimensional tables.

Dedre Gentner and Susan Goldin-Meadow (eds): Language in Mind: Advances in␣the Study of Language and Thought.

Despite ‘scientific’ developments and experimental research in the fields of linguisticand cognitive sciences, the relationship between language, thought and cognition isfar from settled. On the contrary, these inquiries seem to make the issue ever moreelusive. Empirical investigations are not in themselves decisive, since they arefounded on assumptions and specific modes of posing questions, which go deep intodiverse philosophical musings. This is not to deny the role of preconceptions in anyserious scientific work, but the results can sometimes be just as conclusive as thereckonings of Tweedledum-Tweedledee in ‘‘If it was so, it might be; and if it were so,it would be: but as it isn’t, it ain’t’’ (Lewis Carrol’s Through the Looking Glass). This‘uncertainty’ in experimental physics, way back in 1897, would have almost denied usthe discovery of electrons, had not J. J. Thomson’s sensibilities superseded thepositivistic outlook of his contemporary Walter Kaufmann (Weinberg, 1992). Butthen, ‘‘If science is the constellation of facts, theories, and methods ..., then scientistsare the [wo]men who, successfully or not, have striven to contribute one or anotherelement to that particular constellation. Scientific development becomes the piece-meal process by which these items have been added, singly and in combination, tothe ever growing stockpile that constitutes scientific technique and knowledge’’(Kuhn, 1996, pp. 1–2). The success of the present collection of articles by cognitivescientists and linguists lies in the reconfirmation of these facts. Reposing one of theoriginal questions of the discipline—the Sapir-Whorf Hypothesis that contemplatesthe possible influence of language on thought—the book makes latest approachesand research in cognitive linguistics undergo test by fire.As the editors rightly note, the complexity of the relationship betweenthought and language derives from its multi-faceted nature. The varied facets ofthis association—language as ‘‘lens’’, ‘‘tool kit’’ or ‘‘language for speaking’’etc.—invoke interesting but equally varied questions. Further, differences over whataspects of language are relative to diverse linguistic communities, what are universal

Fact constellation 스키마와 트리 기반 XML 모델을 적용한 실험실 레벨의 단백질 데이터 통합 기법

유전자 및 단백질간의 복잡한 상호작용에 의해 기능이 결정되는 생명정보 데이터의 특성으로 인하여 생명정보 데이터 분석을 위해서는 이질적인 데이터를 통합적으로 분석할 수 있는 통합시스템이 요구된다. 따라서 이 논문에서는 생물학 실험실 레벨에서 단백질 구조 관련 데이터를 통합할 수 있도록 XML 모델기반에 웨어하우스 미디에이터 통합시스템을 제안한다. 제안 시스템은 fact constellation 모델을 기반하여 이질적인 소스에 대한 통합 모델링을 진행하고 통합 스키마를 XML 스키마로 변환하여 유지한다. 또한 통합 데이터베이스에 포함된 소스 데이터의 변경 및 출처에 대한 추적 관리를 위해 데이터의 점진적 갱신방법과 서열에 대한 버전관리를 이용한다. 실제로 이 시스템을 단백질 구조(PDB), 서열(Swiss-Prot)과 도메인 분류데이터(CATH) 통합에 적용한 통합 모델링 과정을 보여준다. With the explosion of bioinformatics data such proteins and genes, biologists need a integrated system to analyze and organize large datasets that interact with heterogeneous types of biological data. In this paper, we propose a integration system based on a mediated data warehouse architecture using a XML model in order to combine protein related data at biology laboratories. A fact constellation model in this system is used at a common model for integration and an integrated schema it translated to a XML schema. In addition, to track source changes and provenance of data in an integrated database employ incremental update and management of sequence version. This paper shows modeling of integration for protein structures, sequences and classification of structures using the proposed system.