Data Mart Research Articles

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.

Insights from Adopting a Data Commons Approach for Large-scale Observational Cohort Studies: The California Teachers Study.

Large-scale cancer epidemiology cohorts (CEC) have successfully collected, analyzed, and shared patient-reported data for years. CECs increasingly need to make their data more findable, accessible, interoperable, and reusable, or FAIR. How CECs should approach this transformation is unclear. The California Teachers Study (CTS) is an observational CEC of 133,477 participants followed since 1995-1996. In 2014, we began updating our data storage, management, analysis, and sharing strategy. With the San Diego Supercomputer Center, we deployed a new infrastructure based on a data warehouse to integrate and manage data and a secure and shared workspace with documentation, software, and analytic tools that facilitate collaboration and accelerate analyses. Our new CTS infrastructure includes a data warehouse and data marts, which are focused subsets from the data warehouse designed for efficiency. The secure CTS workspace utilizes a remote desktop service that operates within a Health Insurance Portability and Accountability Act (HIPAA)- and Federal Information Security Management Act (FISMA)-compliant platform. Our infrastructure offers broad access to CTS data, includes statistical analysis and data visualization software and tools, flexibly manages other key data activities (e.g., cleaning, updates, and data sharing), and will continue to evolve to advance FAIR principles. Our scalable infrastructure provides the security, authorization, data model, metadata, and analytic tools needed to manage, share, and analyze CTS data in ways that are consistent with the NCI's Cancer Research Data Commons Framework. The CTS's implementation of new infrastructure in an ongoing CEC demonstrates how population sciences can explore and embrace new cloud-based and analytics infrastructure to accelerate cancer research and translation.See all articles in this CEBP Focus section, "Modernizing Population Science."

Integration of textual VoC into a CX data model for business intelligence use in B2C

Customer experience (CX) focuses on customer feedback. CX is a holistic construct which contains different perceptual elements such as satisfaction and loyalty, but also emotions or personality. Customers share their opinions, which contain these elements also in textual expressions through different channels, known in research as Voice of Customer (VoC). Currently, VoC is collected mainly in customer surveys and manually evaluated, or through simple quantitative measurement from data scattered in various systems at the end of a customer journey. To bridge this gap, we designed a multidimensional CX data model for integrated storage of all customers’ data from structured and textual sources. A consolidated CX measurement to monitor elements of CX during the entire customer journey from the customer perspective is proposed to serve as business intelligence. The artefact offers a selfcontained expandable data mart affordable to implement in small and medium B2C enterprises. Companies can now manage customer relationships and future performance more automatically and effectively thanks to integrated information mined from texts, combined with other data from internal systems and shared across the company in unified reporting.

DATA MART DESIGN IN BKPP BANDUNG USING FROM ENTERPRISE MODELS TO DIMENSIONAL MODELS METHOD

The data warehouse is a single data storage that is complete and consistent with subject-oriented, integrated, non-volatile, and time-variant characteristics that can be used to support decisions. While Data mart is a subset of a data warehouse that supports the information needs of certain departments or business process functions. Badan Kepegawaian, Pendidikan Pelatihan (BKPP) of Bandung Municipality has a history of employee mutation transactions that have not been optimally utilized so it will be very useful if the data can be made into a data mart. This data mart development method uses the From Enterprise Models to Dimensional Models method as its design method and the Bottom-Up Approach as an approach in developing a data mart. This data mart developed using the PostgreSQL database and the PHP language. The purpose of this research is to develop human resource data mart by optimizing the utilization of historical data from employee mutation transactions. The results of this research are human resources data mart that accommodates historical data on employee mutation transactions and produces information that is useful for the Top Management to support the making decision.

Conceptual model of operational–analytical data marts for big data processing

Operational data marts that basically constitute slices of thematic narrowly-focused information are designed to provide operational access to big data sources due to consolidation and ranking of information resources based on their relevance. Unlike operational data marts dependent on the sources, analytical data marts are considered as independent data sources created by users to provide structuring of data for the tasks being solved. Therefore, the conceptual model of operational-analytical data marts allows combining the concepts of operational and analytical data marts to generate an analytical cluster that shall act as the basis for quick designing, development and implementation of data models.

Інформаційна взаємодія у розрізнених інформаційних системах ситуаційних центрів

The article is devoted to the problem of information exchange between automated systems of situational centers (SC) of the government authorities of the Security and Defence Sector (SDS) of Ukraine. The prob-lem is extremely topical today, since in the event of nationwide threats (military intervention, terrorism, emergencies), the SDS should work as a single well-coordinated mechanism. Therefore, the creation of the SC SDS system and the establishment of information exchange between them is a prerequisite for this. The paper assesses the most common types of data integration in information systems (IS), such as data ware-houses, data federation and service-oriented approach. As the most modern approach to information inter-action of disparate ISs, a service-oriented approach is proposed. It is noted that for data exchange it is inappropriate to get access to “other” IS and independently search for the necessary information there. Alternatively, it is proposed to isolate the data exchange subject area (SbA) from the IS subject areas of various SCs and to determine the topics necessary for the exchange. Next, for each topic, to build a the-matic view of grouped, analytical data by the type of data mart. Such views will become a data source for the corresponding services in SOA and can be used in Publish/Subscribe and Request/Response modes. The paper considers a method and proposes an algorithm for the interaction of ISs of disparate SCs based on the use of the ontology of SbA data exchange and SOA. The MIP4-IES family of data exchange specifi-cations of the NATO Multilateral Interoperability Program (MIP) is taken as an example. On the basis of legislative documents in the field of emergency response, an ontology of data exchange between the IS of the SC in response to emergency situations has been built. Examples of topics and thematic views are given that can be constructed on the basis of the constructed SbA ontology for data exchange.

Diplopia and Strabismus After Corneal Refractive Surgery.

Diplopia and strabismus are known complications after corneal refractive surgery (CRS). Within the U.S. Armed Forces, refractive surgery is used to improve the operational readiness of the service member, and these complications could cause significant degradation to their capability. This study was performed in order to identify the incidence of strabismus and diplopia following CRS within the U.S. Military Health System. A retrospective review of all patients who underwent photorefractive keratectomy (PRK) or laser-assisted in situ keratomileusis (LASIK) in the Department of Defense from January 2006 through September 2013 was designed and approved by the Naval Medical Center Portsmouth Institutional Review Board. The military health system data mart was queried for all patients who underwent one of these procedures and subsequently had an International Classification of Disease-9 code for any strabismus or diplopia through 2014 allowing at least 1 year of follow-up. We then calculated the incidence of both diplopia and strabismus for these procedures as the primary measure and the overall prevalence as a secondary measure. A total of 108,157 patients underwent PRK or LASIK during our study period with 41 of these patients subsequently having a diagnosis of diplopia or strabismus. After chart review, 16 of these patients were excluded resulting in 25 patients for inclusion in either the strabismus (23 patients, 0.02%) or diplopia (3 patients, 0.003%) cohorts with one patient having both. Of the 23 patients with postoperative strabismus, 4 were new cases giving an incidence of 0.004% and 2 new cases of diplopia for an incidence of 0.002%. Diplopia and strabismus are rare complications after CRS in the U.S. military population. These procedures continue to increase the operational readiness of our service members with minimal risk of these potentially debilitating complications. Overall, this study provides support for the continued use of PRK and LASIK despite study limitations related to the use of large databases for retrospective review. Future prospective studies using delineated preoperative and postoperative examinations with sensorimotor testing included may be able to resolve the limitations of this study.

A Data warehouse Testing Strategy for Banks

Data Quality, Database Testing, and ETL Testing are all different techniques for testing Data Warehouse Environment. Testing the data became very important as it should be guaranteed that the data is accurate for further manipulation and decision making. A lot of approaches and tools came up supporting and defining the test cases to be used, their functionality, and if they could be automated or not. The most trending approach was the automating of testing data warehouse using tools, the tools started firstly by supporting only the automation of running the scripts helping the developers to write the test case just once and run it multiple times, then the tools developed and modified to automate the creation of the testing scripts and offer their service as a complete application that supports the creation and running of the test cases claiming that the user can work without the need of expertise and high technicality and just by being an end user using the tool’s GUI. Banking sector differs completely than any other industry, as data warehouse in banking sectors collects data from multiple sources and multiple branches with different data formats, and quality that should then be transformed and loaded in the data warehouse and classified into some data marts to be used in different dashboards and projects that depend on high quality and accurate data for further decision making and predictions. In this paper we propose a strategy for data warehouse testing, that automates all the test cases needed in banking environment

PCSK9 Inhibitor Use in the Real World: Data From the National Patient-Centered Research Network.

BackgroundPCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitors effectively lower LDL (low‐density lipoprotein) cholesterol and have been shown to reduce cardiovascular outcomes in high‐risk patients. We used real‐world electronic health record data to characterize use of PCSK9 inhibitors, in addition to standard therapies, according to cardiovascular risk status.Methods and ResultsData were obtained from 18 health systems with data marts within the National Patient‐Centered Clinical Research Network (PCORnet) using a common data model. Participating sites identified >17.5 million adults, of whom 3.6 million met study criteria. Patients were categorized into 3 groups: (1) dyslipidemia, (2) untreated LDL ≥130 mg/dL, and (3) coronary artery disease or coronary heart disease. Demographics, comorbidities, estimated 10‐year atherosclerotic cardiovascular disease risk, and lipid‐lowering pharmacotherapies were summarized for each group. Participants’ average age was 62 years, 50% were female, and 11% were black. LDL cholesterol ranged from 85 to 151 mg/dL. Among patients in groups 1 and 3, 54% received standard lipid‐lowering therapies and a PCSK9 inhibitor was prescribed in <1%. PCSK9 inhibitor prescribing was greatest for patients with coronary artery disease or coronary heart disease and, although prescribing increased during the study period, overall PCSK9 inhibitor prescribing was low.ConclusionsWe successfully used electronic health record data from 18 PCORnet data marts to identify >3.6 million patients meeting criteria for 3 patient groups. Approximately half of patients had been prescribed lipid‐lowering medication, but <1% were prescribed PCSK9 inhibitors. PCSK9 inhibitor prescribing increased over time for patients with coronary artery disease or coronary heart disease but not for those with dyslipidemia.

Reducing Query Processing Time for Non-Synonymous Materialized Queries With Differed Criteria

Results of OLAP queries for strategic decision making are generated using warehouse data. For frequent queries, processing overhead increases as same results are generated by traversing through huge volume of warehouse data. Authors suggest saving time for frequent queries by storing them in a relational database referred as MQDB, along with its result and metadata information. Incremental updates for synonymous materialized queries are done using data marts. This article focusses on saving processing time for non-synonymous queries with differed criteria. Criteria is the query condition specified with ‘where' or a ‘having' clause apart from equijoin condition. Defined rules will determine if new results can be derived from existing stored results. If criteria of fired query are a subset of criteria in stored query, results are extracted from existing results using MINUS operation. When criteria are a superset of stored query criteria, new results are appended to existing results using the UNION operation.

Analysis and Design of Data Mart Decision Support Systems at PT Marlindo Tirta Nusantara

The role of information technology in each company is very influential in providing complete and accurate information. With a data warehouse or data mart the company can utilize important asset data to present the information needed.. Shipping firms nowadays are in operation with extremely competitive and challenging environment. Vast volume of knowledge is generated from various operational systems and these are used for determination of several business issues that needed imperative handling. The results obtained from this study are data marts that simplify and accelerate the provision of data and information to support decision making, so as to provide a basis for developing DSS and EIS applications. Conclusions obtained by the data warehouse or data mart can provide complete, accurate and integrated information as a basis for consideration for the executive in making decisions so that decisions taken are based on real facts owned by the company.

Enhancing the involvement of decision makers in data mart design

The design phase of a data warehousing project remains difficult for both decision makers and requirements analysts. In this paper, we tackle this difficulty through two contributions. First, we propose a natural language based and goal-oriented template for requirements specification that includes all concepts of the decision-making process. The use of familiar concepts and natural language makes our template more accessible and helps decision makers in validating the specified requirements, which avoids producing data mart that does not meet their needs. Secondly, we propose a decision-making ontology that provides for a systematic decomposition of decision-making goals, which allows new requirements to emerge. This automatic requirements elicitation helps analysts to overcome their lack of domain knowledge, which avoids producing erroneous requirements.

Enhancing the involvement of decision makers in data mart design

The design phase of a data warehousing project remains difficult for both decision makers and requirements analysts. In this paper, we tackle this difficulty through two contributions. First, we propose a natural language based and goal-oriented template for requirements specification that includes all concepts of the decision-making process. The use of familiar concepts and natural language makes our template more accessible and helps decision makers in validating the specified requirements, which avoids producing data mart that does not meet their needs. Secondly, we propose a decision-making ontology that provides for a systematic decomposition of decision-making goals, which allows new requirements to emerge. This automatic requirements elicitation helps analysts to overcome their lack of domain knowledge, which avoids producing erroneous requirements.

Linking Context to Data Warehouse Design

Data warehouses are now widely used for analysis and decision support purposes. The availability of software solutions, which are more and more user-friendly and easy to manipulate has made it possible to extend their use to end users who are not specialists in the field of business intelligence. The purpose of this article is to provide an approach that assists non-expert users in the data warehouse design process and integrates their contextual data. As well as to provide a method that assists non-expert users in data warehouse design process while incorporating their contextual data. Our proposal consists of a context model and a comprehensive Data Warehouse construction method that attaches the context to data warehouses and uses it to produce customized data marts adapted to the decision makers context.

Building federated data warehouse schema using web service approach

The data warehouse is considered as a repository that stores data from disparate sources. It is used mainly in the process of decision making. Because of the diversity of the applications, their heterogeneity and their physical spacing, building a data warehouse, to make decisions in a federated architecture, is a very complicated task. To overcome this problem, we propose a new approach for the design of a federated data warehouse schema based on the web services. The latter are applied because of their capacity to solve many technical problems related to the integration of data source in a federated environment. The proposed solution is based on the bottom-up approach where the data warehouse schema is generated from the set schemas of the data mart. It is composed by two steps. In the first one, a web service is used to generate the schemas of the data mart one for each data source. In the second step, a second web service is used to merge the generated schemas to build the final schema of the data warehouse.

Requirements-based approach for multidimensional design

Requirements analysis is a key step in the process of the data warehouse design. It is the key element for the success of a data warehouse project since at the end of this phase, the multidimensional elements will be defined and the model designed. This paper presents the set of requirements driven methods for designing data warehouses and reveals the pros and cons of the two main used approaches. Then, it introduces a new user driven method that leverages all the potential of this approach. This new method is based not only on orienting users in their research via semantic analysis and parsing their search history, but also on the evolution of data marts.

Building federated data warehouse schema using web service approach

The data warehouse is considered as a repository that stores data from disparate sources. It is used mainly in the process of decision making. Because of the diversity of the applications, their heterogeneity and their physical spacing, building a data warehouse, to make decisions in a federated architecture, is a very complicated task. To overcome this problem, we propose a new approach for the design of a federated data warehouse schema based on the web services. The latter are applied because of their capacity to solve many technical problems related to the integration of data source in a federated environment. The proposed solution is based on the bottom-up approach where the data warehouse schema is generated from the set schemas of the data mart. It is composed by two steps. In the first one, a web service is used to generate the schemas of the data mart one for each data source. In the second step, a second web service is used to merge the generated schemas to build the final schema of the data warehouse.

Developing a multi-center clinical data mart of ACEI and ARB for real-world evidence (RWE)

BackgroundRandomized controlled trials can be expensive and time-consuming, leading to medical researchers utilizing real-world evidence (RWE) based on already-collected data. We aimed to conduct various RWE studies on angiotensin-converting enzyme inhibitors (ACEI) and angiotensin II receptor blocker (ARB), commonly used as first-line therapy for blood pressure, and to develop a multi-center clinical data mart (CDM) of ACEI/ARB for various clinical purposes.MethodsData from electronic medical records of St. Mary’s Hospital and the Seoul National University Hospital were collected. We obtained blood and urine test results of patients within the 30 days prior to their first prescription of ACEI or ARB, as well as the first date of diagnosis and presence of various chronic and cardiovascular diseases using the International Classification of Diseases-10 classification. One researcher managed data quality and collation for each hospital in order to facilitate patient anonymity. When results were unclear, the responsible investigator for each hospital attempted to resolve ambiguities by direct chart review.ResultsA total of 102,333 patients who were prescribed ACEI or ARB for the first time were included (21,481 ACEI, 80,551 ARB, and 301 both). Our ACEI/ARB-CDM included short-term studies (within 12 months) to observe changes in various blood or urinary laboratory test values after the initial prescription of ACEI or ARB and long-term studies to confirm the incidence of various diseases.ConclusionWe established a CDM of RWE for ACEI/ARB prescription, which included various clinical studies. As we accumulate experience in this process, we expect that the use of RWE research will grow and develop.

Automated feature selection of predictors in electronic medical records data.

The use of Electronic Health Records (EHR) for translational research can be challenging due to difficulty in extracting accurate disease phenotype data. Historically, EHR algorithms for annotating phenotypes have been either rule-based or trained with billing codes and gold standard labels curated via labor intensive medical chart review. These simplistic algorithms tend to have unpredictable portability across institutions and low accuracy for many disease phenotypes due to imprecise billing codes. Recently, more sophisticated machine learning algorithms have been developed to improve the robustness and accuracy of EHR phenotyping algorithms. These algorithms are typically trained via supervised learning, relating gold standard labels to a wide range of candidate features including billing codes, procedure codes, medication prescriptions and relevant clinical concepts extracted from narrative notes via Natural Language Processing (NLP). However, due to the time intensiveness of gold standard labeling, the size of the training set is often insufficient to build a generalizable algorithm with the large number of candidate features extracted from EHR. To reduce the number of candidate predictors and in turn improve model performance, we present an automated feature selection method based entirely on unlabeled observations. The proposed method generates a comprehensive surrogate for the underlying phenotype with an unsupervised clustering of disease status based on several highly predictive features such as diagnosis codes and mentions of the disease in text fields available in the entire set of EHR data. A sparse regression model is then built with the estimated outcomes and remaining covariates to identify those features most informative of the phenotype of interest. Relying on the results of Li and Duan (1989), we demonstrate that variable selection for the underlying phenotype model can be achieved by fitting the surrogate-based model. We explore the performance of our methods in numerical simulations and present the results of a prediction model for Rheumatoid Arthritis (RA) built on a large EHR data mart from the Partners Health System consisting of billing codes and NLP terms. Empirical results suggest that our procedure reduces the number of gold-standard labels necessary for phenotyping thereby harnessing the automated power of EHR data and improving efficiency.

Integrative LHS for precision medicine research: A shared NIH and Taiwan CIMS experience.

IntroductionPrecision medicine is an important milestone toward the attainment of personalized medicine. A learning health system (LHS) may facilitate the evidence collection and knowledge generation process for disease‐based research and for the diagnosis, classification, or treatment of each disease subtype to improve patient care.MethodsThe LHS design and implementation used by Taichung Veterans General Hospital (TCVGH) in Taiwan for their newly funded precision medicine research, a dementia registry study, was modeled from an LHS developed at the National Institutes of Health in the United States. This Clinical Informatics and Management System (CIMS), including its subsystems, facilitates and enhances operations associated with the institutional review board, clinical research data collection and study management, the hospital biobank, and the participating health research centers to support their precision medicine research aimed at improving patient care.ResultsThe implementation of a shared‐design, full‐cycle LHS with an enhanced CIMS, combined with hospital‐based real‐world data marts, has made the TCVGH dementia registry study a reality. The research data, including clinical assessment and genomics analysis information collected in CIMS, combined with data marts, are the foundation of the TCVGH dementia registry for outcome analyses. These high‐quality datasets are useful for clinical validation, new hypotheses, and knowledge generation, leading to new clinical recommendations or guidelines for better patient treatment and care. The cyclic data flow supports the full‐cycle LHS for TCVGH's dementia research to improve the care of elderly patients.ConclusionsKnowledge generation requires high‐quality research and health care datasets. While the details of LHS implementation methods in the United States and Taiwan may differ slightly, the LHS concept design and basic system architecture, with improved CIMSs, were proven feasible. As a result, learning health processes in support of translational research and the potential for improvement in patient care were significantly facilitated.