Integrated Data Warehouse Research Articles

Health Care Resource Utilization and Costs for Adults With Mild Traumatic Brain Injury With Chronic Vestibular Impairment

ObjectiveTo quantify the economic burden of all-cause health care resource utilization (HCRU) among adults with and without chronic vestibular impairment (CVI) after a mild traumatic brain injury (mTBI). DesignRetrospective matched cohort study. SettingIQVIA Integrated Data Warehouse. ParticipantsPeople with mTBI+CVI (n=20,441) matched on baseline age, sex, year of mTBI event, and Charlson Comorbidity Index (CCI) score to people with mTBI only (n=20,441) (N=40,882). InterventionsNot applicable. Main Outcome MeasuresAll-cause health HCRU and costs at 12 and 24 months post mTBI diagnosis. ResultsPeople with mTBI+CVI had significantly higher all-cause HCRU and costs at both time points than those with mTBI only. Multivariable regression analysis showed that, when controlling for baseline variables, costs of care were 1.5 times higher for mTBI+CVI than mTBI only. ConclusionsPeople who developed CVI after mTBI had greater overall HCRU and costs for up to 2 years after the injury event compared with people who did not develop CVI after controlling for age, sex, region, and CCI score. Further research on access to follow-up services and effectiveness of interventions to address CVI is warranted.

Physician decision support system for idiopathic sudden sensorineural hearing loss patients.

Idiopathic sudden sensorineural hearing loss (ISSNHL) is an emergency disease, and its pathogenesis is still largely unknown, making it difficult to diagnose and develop a therapeutic strategy. To predict the treatment outcomes and further customize the treatment strategy, we used a physician decision support system that incorporates complex information from electronic health records. We first developed the infrastructure of the physician decision support system, including an integrated data warehouse and an automatic data de-identification workflow. We next conducted a cohort study to evaluate the treatment outcomes of 757 ISSNHL patients using the modified Siegel's criteria. The complete recovery (<25 dB) as a hearing outcome for ISSNHL patients was compared based on pretreatment hearing grades and disease onset with adjusted for age and sex after treatment initiation. The results showed that a complete recovery hearing outcome based on pretreatment hearing grades and disease onset in consideration of age and sex was associated with a low risk of pretreatment hearing Grade 2 (26-45 dB) (adjusted odds ratio 12.3, 95% confidence interval [CI]: 4.8-31.3) and disease onset ≤7 days (adjusted odds ratio 13.9, 95% CI: 4.2-45.8), respectively. Hearing recovery outcomes among complete recovery and noncomplete recovery (>25 dB) subjects according to pretreatment hearing grades were 32.9% (Grade 2 or 26-45 dB HL), 25.4% (Grade 3 or 46-75 dB HL), 31.1% (Grade 4 or 76-90 dB), and 4.5% (Grade 5, or >90 dB HL) (p < 0.0001). Patients with pretreatment hearing Grade 2 who received treatment within ≤7 days of disease onset had the highest rate of complete recovery (32.9%, 23/70). In summary, using the physician decision support system, we successfully identified two predictors, the pretreatment hearing Grade 2 (26-45 dB) and treatment within ≤7 days of disease onset, associated with the highest odds of achieving complete recovery (<25 dB) of hearing in patients with ISSNHL.

PUK5 OVERACTIVE BLADDER IN THE UNITED STATES LONG-TERM CARE SETTING IS ASSOCIATED WITH SIGNIFICANT BURDEN AND INCREASED HEALTHCARE RESOURCE UTILIZATION AND COSTS: A RETROSPECTIVE DATABASE STUDY

An estimated 30 million Americans are affected by bothersome symptoms of overactive bladder (OAB). Approximately 70% of long-term care (LTC) patients suffer from urinary incontinence. We evaluated one-year, all-cause healthcare resource utilization (HCRU) and costs among patients with or without OAB. A retrospective cohort analysis was conducted using claims from IQVIA’s LTC data linked to IQVIA’s integrated data warehouse. Adult patients with ≥2 OAB/urinary incontinence medication prescriptions in LTC were propensity score matched to patients without an OAB medication in LTC between May 2013 and May 2018. Differences in baseline demographic and clinical characteristics used for propensity score matching were examined using standardized differences. HCRU and costs measured during the pre- and post-index periods were compared between OAB and non-OAB cohorts. Adjusted cost-ratio for OAB vs non-OAB cohort was estimated using generalized estimating equation model. Post matching, OAB and non-OAB cohorts had 159,785 patients; mean age 75 years. A statistically significant (p<0.05) higher proportion of patients in OAB (vs non-OAB) cohort were females (74.0% vs 66.8%), had baseline prescriptions for medications metabolized by CYP2D6 (73.8% vs 67.3%) and antidepressants (54.3% vs 45.7%), had follow-up physician office visits (71.3% vs 65.3%) and outpatient visits related to OAB/incontinence (26.2% vs 6.4%), had urinary catheterizations (58.8% vs 39.6%) and falls/fractures (30.5% vs 25.5%). Patients with OAB were prescribed a median of 9 medications/month and 84.0% of patients were exposed to a non-urinary anticholinergic. Mean total all-cause healthcare and pharmacy costs were significantly higher ($15,870 vs $12,975 and $8,271 vs $5,676; standardized differences >0.1) for OAB vs non-OAB cohort. Adjusted analyses demonstrated 17% higher total costs in OAB vs non-OAB cohort (cost ratio: 1.17, 95% CI: 1.15-1.18; p<0.0001). OAB is associated with significant disease and economic burden in the US LTC setting, indicating a need for better treatment management.

Impact of a Structured Patient-Support Program on Adherence and Persistence with Basal Insulin Therapy at 12 Months

The COACH patient-support program was launched in 2015 to support and educate patients prescribed insulin glargine 300 U/mL (Gla-300). The study objective was to assess program impact on adherence and persistence among patients with T2D on Gla-300 therapy. A retrospective cohort study was conducted, linking data from COACH to patient data in the IQVIA Integrated Data Warehouse. Overall inclusion criteria for patients identified from 2/1/2016-7/31/2016 were: ≥1 T2D diagnosis code, aged ≥18 years, ≥1 pharmacy claim for Gla-300 (first claim as index date), continuous database activity for ≥180 days preceding the 6-month pre-index, and pharmacy stability in the 6-month pre-index. Exclusion criteria were: ≥1 pharmacy claim for another basal insulin ≤14 days preceding index, and data-quality issues. Patients from the COACH Participant cohort (active participants of the program) were directly matched to a Non-COACH cohort of patients not enrolled in COACH. Patients who were insulin naive, switched from any other basal insulin to Gla-300, and with high levels of engagement to COACH were analyzed as subcohorts. Treatment persistence and adherence were measured over a 12-month follow-up. The matched cohorts included 544 patients each. Over 12 months, a significantly greater proportion of COACH Participants were persistent (48.5% vs. 42.1%; p=0.0309) and adherent (68.0% vs. 61.4%; p=0.0201) to Gla-300 compared with patients in the Non-COACH cohort. In multivariate adjusted analyses, the odds of being adherent to index medication were 1.81 times higher (OR 1.81; p=0.0002) and the risk of discontinuation was 40% lower (HR 0.60; p&amp;lt;0.0001) for COACH Participants. Similar results were found among naive and switching subcohorts. A higher level of engagement with COACH was associated with a lower risk of discontinuation. Active participation in the COACH patient-support program was associated with improved adherence and persistence to Gla-300. Disclosure F.L. Zhou: Employee; Self; Sanofi. Stock/Shareholder; Self; Sanofi. Employee; Spouse/Partner; Lexicon Pharmaceuticals, Inc.. Stock/Shareholder; Spouse/Partner; Lexicon Pharmaceuticals, Inc. J. Yeaw: Other Relationship; Self; Sanofi US. S. Karkare: None. M. DeKoven: None. T.S. Reid: Speaker's Bureau; Self; Novo Nordisk Inc.. Advisory Panel; Self; Novo Nordisk Inc.. Speaker's Bureau; Self; Sanofi. Advisory Panel; Self; Sanofi, Janssen Pharmaceuticals, Inc.. Speaker's Bureau; Self; Janssen Pharmaceuticals, Inc.. Consultant; Self; AstraZeneca, Intarcia Therapeutics, Inc.. Speaker's Bureau; Self; Lilly.

Identifying Unmet Treatment Needs for Patients With Osteoporotic Fracture: Feasibility Study for an Electronic Clinical Surveillance System.

BackgroundTraditional clinical surveillance relied on the results from clinical trials and observational studies of administrative databases. However, these studies not only required many valuable resources but also faced a very long time lag.ObjectiveThis study aimed to illustrate a practical application of the National Taiwan University Hospital Clinical Surveillance System (NCSS) in the identification of patients with an osteoporotic fracture and to provide a high reusability infrastructure for longitudinal clinical data.MethodsThe NCSS integrates electronic medical records in the National Taiwan University Hospital (NTUH) with a data warehouse and is equipped with a user-friendly interface. The NCSS was developed using professional insight from multidisciplinary experts, including clinical practitioners, epidemiologists, and biomedical engineers. The practical example identifying the unmet treatment needs for patients encountering major osteoporotic fractures described herein was mainly achieved by adopting the computerized workflow in the NCSS.ResultsWe developed the infrastructure of the NCSS, including an integrated data warehouse and an automatic surveillance workflow. By applying the NCSS, we efficiently identified 2193 patients who were newly diagnosed with a hip or vertebral fracture between 2010 and 2014 at NTUH. By adopting the filter function, we identified 1808 (1808/2193, 82.44%) patients who continued their follow-up at NTUH, and 464 (464/2193, 21.16%) patients who were prescribed anti-osteoporosis medications, within 3 and 12 months post the index date of their fracture, respectively.ConclusionsThe NCSS systems can integrate the workflow of cohort identification to accelerate the survey process of clinically relevant problems and provide decision support in the daily practice of clinical physicians, thereby making the benefit of evidence-based medicine a reality.

Description of a Method to Obtain Complete One-Year Follow-Up in the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry

The Centers for Medicare and Medicaid Services National Coverage Determination requires centers performing transcatheter aortic valve implantation (TAVI) to report clinical outcomes up to 1 year. Many sites encounter challenges in obtaining complete 1-year follow-up. We report our process to address this challenge. A multidisciplinary process involving clinical personnel, data and quality managers, and research coordinators was initiated to collect TAVI data at baseline, 30 days, and 1 year. This process included (1) planned clinical follow-up of all patients at 30 days and 1 year; (2) query of health-care system-wide integrated data warehouse (IDW) to ascertain last date of clinical contact within the system for all patients; (3) online obituary search, cross-referencing for unique patient identifiers to determine if mortality occurred in remaining unknown patients; and (4) phone calls to remaining unknown patients or patients' families. Between January 2012 and December 2016, 744 patients underwent TAVI. All 744 patients were eligible for 30-day follow-up and 546 were eligible for 1-year follow-up. At routine clinical follow-up of 22 of 744 (3%) patients at 30 days and 180 of 546 (33%) patients at 1 year had unknown survival status. The integrated data warehouse query confirmed status-alive for an additional 1 of 22 patients at 30 days (55%) and 91 of 180 patients at 1 year (51%). Obituaries were identified for 23 of 180 additional patients at 1 year (13%). Phone contact identified the remaining unknown patients at 30 days and 1 year, resulting in 100% known survival status for patients at 30 days (744 of 744) and at 1 year (546 of 546). In conclusion, using a comprehensive approach, we were able to determine survival status in 100% of patients who underwent TAVI.

CHOmine: an integrated data warehouse for CHO systems biology and modeling.

The last decade has seen a surge in published genome-scale information for Chinese hamster ovary (CHO) cells, which are the main production vehicles for therapeutic proteins. While a single access point is available at www.CHOgenome.org, the primary data is distributed over several databases at different institutions. Currently research is frequently hampered by a plethora of gene names and IDs that vary between published draft genomes and databases making systems biology analyses cumbersome and elaborate. Here we present CHOmine, an integrative data warehouse connecting data from various databases and links to other ones. Furthermore, we introduce CHOmodel, a web based resource that provides access to recently published CHO cell line specific metabolic reconstructions. Both resources allow to query CHO relevant data, find interconnections between different types of data and thus provides a simple, standardized entry point to the world of CHO systems biology. Database URL: http://www.chogenome.org

Mining a database of single amplified genomes from Red Sea brine pool extremophiles-improving reliability of gene function prediction using a profile and pattern matching algorithm (PPMA).

Reliable functional annotation of genomic data is the key-step in the discovery of novel enzymes. Intrinsic sequencing data quality problems of single amplified genomes (SAGs) and poor homology of novel extremophile's genomes pose significant challenges for the attribution of functions to the coding sequences identified. The anoxic deep-sea brine pools of the Red Sea are a promising source of novel enzymes with unique evolutionary adaptation. Sequencing data from Red Sea brine pool cultures and SAGs are annotated and stored in the Integrated Data Warehouse of Microbial Genomes (INDIGO) data warehouse. Low sequence homology of annotated genes (no similarity for 35% of these genes) may translate into false positives when searching for specific functions. The Profile and Pattern Matching (PPM) strategy described here was developed to eliminate false positive annotations of enzyme function before progressing to labor-intensive hyper-saline gene expression and characterization. It utilizes InterPro-derived Gene Ontology (GO)-terms (which represent enzyme function profiles) and annotated relevant PROSITE IDs (which are linked to an amino acid consensus pattern). The PPM algorithm was tested on 15 protein families, which were selected based on scientific and commercial potential. An initial list of 2577 enzyme commission (E.C.) numbers was translated into 171 GO-terms and 49 consensus patterns. A subset of INDIGO-sequences consisting of 58 SAGs from six different taxons of bacteria and archaea were selected from six different brine pool environments. Those SAGs code for 74,516 genes, which were independently scanned for the GO-terms (profile filter) and PROSITE IDs (pattern filter). Following stringent reliability filtering, the non-redundant hits (106 profile hits and 147 pattern hits) are classified as reliable, if at least two relevant descriptors (GO-terms and/or consensus patterns) are present. Scripts for annotation, as well as for the PPM algorithm, are available through the INDIGO website.

Real-World Cost-Effectiveness: Lower Cost of Treating Patients to Glycemic Goal with Liraglutide versus Exenatide

IntroductionWhile the liraglutide effect and action in diabetes (LEAD-6) clinical trial compared the efficacy and safety of liraglutide once daily (LIRA) to exenatide twice daily (EXEN) in adult patients with type 2 diabetes, few studies have explored the associated per-patient costs of glycemic goal achievement of their use in a real-world clinical setting.MethodsThis retrospective cohort study used integrated medical and pharmacy claims linked with glycated hemoglobin A1C (A1C) results from the IMS Patient-Centric Integrated Data Warehouse. Patients’ ≥18 years and naïve to incretin therapies during a 6-month pre-index period, with ≥1 prescription for LIRA or EXEN between January 2010 and December 2010, were included. Patients with evidence of insulin use (pre- or post-index) were excluded. Only patients who were persistent on their index treatment during a 180-day post-index period were included. Follow-up A1C assessments were based on available laboratory data within 45 days before or after the 6-month post-index point in time. Diabetes-related pharmacy costs over the 6-month post-index period were captured and included costs for both the index drugs and concomitant diabetes medications.Results234 LIRA and 182 EXEN patients were identified for the analysis. The adjusted predicted diabetes-related pharmacy costs per patient over the 6-month post-index period were higher for LIRA compared to EXEN ($2,002 [95% confidence interval (CI): $1,981, $2,023] vs. $1,799 [95% CI: $1,778, $1,820]; P < 0.001). However, a higher adjusted predicted percentage of patients on LIRA reached A1C < 7% goal (64.4% [95% CI: 63.5, 65.3] vs. 53.6% [95% CI: 52.6, 54.6]; P < 0.05), translating into lower average diabetes-related pharmacy costs per successfully treated patient for LIRA as compared to EXEN ($3,108 vs. $3,354; P < 0.0001).ConclusionsAlthough predicted diabetes-related pharmacy costs were greater with LIRA vs. EXEN, a higher proportion of patients on LIRA achieved A1C < 7%, resulting in a lower per-patient cost of A1C goal achievement with LIRA compared to EXEN.Electronic supplementary materialThe online version of this article (doi:10.1007/s12325-014-0098-8) contains supplementary material, which is available to authorized users.

INDIGO – INtegrated Data Warehouse of MIcrobial GenOmes with Examples from the Red Sea Extremophiles

BackgroundThe next generation sequencing technologies substantially increased the throughput of microbial genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and computational methods are used. Integration of information from different sources is a powerful approach to enhance such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially depends on this annotation but it is hampered by the current lack of suitable information integration and exploration systems for microbial genomes.ResultsWe developed a data warehouse system (INDIGO) that enables the integration of annotations for exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information from Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments. ConclusionsWe developed a data warehouse system, INDIGO, which enables comprehensive integration of information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline. The INDIGO web server is freely available at http://www.cbrc.kaust.edu.sa/indigo.

The development of a data-matching algorithm to define the ‘case patient’

To describe a model that matches electronic patient care records within a given case to one or more patients within that case. his retrospective study included data from all metropolitan Ambulance Victoria electronic patient care records (n=445576) for the time period 1 January 2009-31 May 2010. Data were captured via VACIS (Ambulance Victoria, Melbourne, Vic., Australia), an in-field electronic data capture system linked to an integrated data warehouse database. The case patient algorithm included 'Jaro-Winkler', 'Soundex' and 'weight matching' conditions. he case patient matching algorithm has a sensitivity of 99.98%, a specificity of 99.91% and an overall accuracy of 99.98%. The case patient algorithm provides Ambulance Victoria with a sophisticated, efficient and highly accurate method of matching patient records within a given case. This method has applicability to other emergency services where unique identifiers are case based rather than patient based.

Clinical outcomes research of Shuxuetong injection based on 59 287 cases in hospital information system 

In order to find out the condition of patients who used the Shuxuetong injection and provide reference and guidance for clinical practice, we analyzed the data of related indicators in patients in the HIS based of the frequency analysis methods. In this study, we selected the hospitalized patient information in 20 hospital information systems (HIS). Reference to the integrated data warehouse build mode, including some information such as age, gender, cost categories of patients, route of administration etc. The average age of patients are 59-years-old , 56-years-old age and 71-years-old age are the two high peaks during the age distribution. Patients covered by health insurance accounted for 75.8%; Intravenous infusion in patients accounted for 95.76%; Patient are also diagnosed as cardiovascular and cerebrovascular diseases, such as hypertension, cerebral infarction, coronary heart diseas, combined with other medicines such as aspirin, insulin, heparin etc. Crowd using Shuxuetong injection mostly in the elderly population, most patients covered by health insurance, the route of administration conforms instructions basically, please attention the benefits and risks of the patients in the clinical practice.

FASTER AND BETTER DECISIONS IN CHANGING ENVIRONMENTS USING A HYBRID APPROACH OF DATA WAREHOUSE INTEGRATION

We live in a globalized and fast changing world. The financial crisis means a lot of trouble for governments, administrations, companies and people. Sometimes the environment changes faster than the actors are able to react. So, at the moment Heraclitus’ proverb ‘Nothing is more constant than the change’ is still up-to date, still valid and of high relevance. Companies merge with other companies; build joint ventures and virtual organizations. What has not been changed for a long time is the fact that only successful companies survive. Successful companies could be characterized as companies which are able to make better decisions faster. Therefore it is necessary to have the relevant data at the right time. This means that data for strategic decisions have to be available shortly after a merger or the establishment of a virtual organization for the whole new organization in a unified, complete and consistent form. It is not possible to build a data warehouse all from the scratch because this means a project of several years which is often still not in time and not in budget. A new way to develop an integrated data warehouse is needed. In this paper we show how a hybrid approach of data warehouse integration could look like.DOI: http://dx.doi.org/10.5755/j01.em.17.2.2209

Data Warehouse Implementation of Examination Databases

A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users [2][10]. This was the case at University of Kashmir (UOK) Examination Department, where such a project brought together various attributes of Examination System which included Conduct, Secrecy, Transit, Tabulation, Accounts and other related data sources in to an integrated data warehouse.

YeastMine--an integrated data warehouse for Saccharomyces cerevisiae data as a multipurpose tool-kit.

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) provides high-quality curated genomic, genetic, and molecular information on the genes and their products of the budding yeast Saccharomyces cerevisiae. To accommodate the increasingly complex, diverse needs of researchers for searching and comparing data, SGD has implemented InterMine (http://www.InterMine.org), an open source data warehouse system with a sophisticated querying interface, to create YeastMine (http://yeastmine.yeastgenome.org). YeastMine is a multifaceted search and retrieval environment that provides access to diverse data types. Searches can be initiated with a list of genes, a list of Gene Ontology terms, or lists of many other data types. The results from queries can be combined for further analysis and saved or downloaded in customizable file formats. Queries themselves can be customized by modifying predefined templates or by creating a new template to access a combination of specific data types. YeastMine offers multiple scenarios in which it can be used such as a powerful search interface, a discovery tool, a curation aid and also a complex database presentation format.Database URL: http://yeastmine.yeastgenome.org

TRANSITIONAL ISSUES DURING MIGRATION TO AN INTEGRATED DATA WAREHOUSE SYSTEM: A CASE STUDY

This report presents a case study analysis involving four mid-level managers (academic deans) at a for-profit university, as they experienced a transition from a silo based data management system to a fully integrated data warehouse system. The study revealed that, for these participants, major transitional issues included frustration with lack of access to data that they previously had, an increase in errors from inaccurate data, and increased alienation from the organization due to the dismantling of tacit informal networks built up over time.

Construction and realization of real world integrated data warehouse from HIS on re-evaluation of post-maketing traditional Chinese medicine

To construct real world integrated data warehouse on re-evaluation of post-marketing traditional Chinese medicine for the research on key techniques of clinic re-evaluation which mainly includes indication of traditional Chinese medicine, dosage usage, course of treatment, unit medication, combined disease and adverse reaction, which provides data for reviewed research on its safety,availability and economy,and provides foundation for perspective research. The integrated data warehouse extracts and integrate data from HIS by information collection system and data warehouse technique and forms standard structure and data. The further research is on process based on the data. A data warehouse and several sub data warehouses were built, which focused on patients' main records, doctor orders, diseases diagnoses, laboratory results and economic indications in hospital. These data warehouses can provide research data for re-evaluation of post-marketing traditional Chinese medicine, and it has clinical value. Besides, it points out the direction for further research.

TargetMine, an Integrated Data Warehouse for Candidate Gene Prioritisation and Target Discovery

Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at http://targetmine.nibio.go.jp/.

A Financial Assets and Liabilities Management Support System

This paper describes the design and implementation of a decision support system (DSS) based on the fund dispatching decision viewpoint from the financial division of a business group. An integrated data warehouse is established and the technique of online analytical processing (OLAP) is applied to analyze daily transaction data of an enterprise resource planning system with determined management goal. We adopt the Business Dimensional Lifecycle approach to accomplish the system design and development. The DSS system developed is to provide latest and timely information of financial asset and liability positions in each company within the case business group so that decision makers can have a clear decision support in fund dispatching. While most related researches on fund dispatching focused especially on efficient banking capital management and few studies were done for general financial department of traditional enterprise let alone for the business group, this study has made a progress in this issue and the resultant system is applicable to the similar business group. Keywords: Financial Asset and Liability, Fund Dispatching, Decision Support Systems, OLAP, Data Warehouse

Object-extended OLAP querying

On-line analytical processing (OLAP) systems based on a dimensional view of data have found widespread use in business applications and are being used increasingly in non-standard applications. These systems provide good performance and ease-of-use. However, the complex structures and relationships inherent in data in non-standard applications are not accommodated well by OLAP systems. In contrast, object database systems are built to handle such complexity, but do not support OLAP-type querying well. This paper presents the concepts and techniques underlying a flexible, “multi-model” federated system that enables OLAP users to exploit simultaneously the features of OLAP and object systems. The system allows data to be handled using the most appropriate data model and technology: OLAP systems for dimensional data and object database systems for more complex, general data. This allows data analysis on the OLAP data to be significantly enriched by the use of additional object data. Additionally, physical integration of the OLAP and the object data can be avoided. As a vehicle for demonstrating the capabilities of the system, a prototypical OLAP language is defined and extended to naturally support queries that involve data in object databases. The language permits selection criteria that reference object data, queries that return combinations of OLAP and object data, and queries that group dimensional data according to object data. The system is designed to be aggregation-safe, in the sense that it exploits the aggregation semantics of the data to prevent incorrect or meaningless query results. These capabilities may also be integrated into existing languages. It is shown how to integrate relational and XML data using the technology. A prototype implementation of the system is reported, along with performance measurements that show that the approach is a viable alternative to a physically integrated data warehouse.