Breaking the 80:20 rule in health research using large administrative data sets.

Objective: To explore the application of online analytic processing (OLAP) to improve the efficiency of analytics using large administrative health data sets. Methods: 18 years of administrative health data (1994/95 to 2012/13) were obtained from the Alberta Ministry of Health in Canada. The data sets included hospitalization, ambulatory care and practitioner claims data. Reference files were obtained that provided information including patient demographics, resident postal code, facility, and provider details. Population counts and projections for each year, sex, age were included for rate calculations. These sources were used to develop a data cube using OLAP tools. Results: Time required for analyses was reduced to 5% of that required when comparing run-time for simple queries that did not require linkage of data sets. The data cube negated the need for many intermediary steps for data extraction and analyses for research activities. Conventional methods required over 250 GB of server space for multiple analytic subsets, compared to only 10.3 GB for the data cube. Conclusions: Cross-training in information technology and health analytics is recommended to provide capacity to better leverage OLAP tools which are available with many common applications.

AnFiSA: An open-source computational platform for the analysis of sequencing data for rare genetic disease.

Despite genomic sequencing rapidly transforming from being a bench-side tool to a routine procedure in a hospital, there is a noticeable lack of genomic analysis software that supports both clinical and research workflows as well as crowdsourcing. Furthermore, most existing software packages are not forward-compatible in regards to supporting ever-changing diagnostic rules adopted by the genetics community. Regular updates of genomics databases pose challenges for reproducible and traceable automated genetic diagnostics tools. Lastly, most of the software tools score low on explainability amongst clinicians. We have created a fully open-source variant curation tool, AnFiSA, with the intention to invite and accept contributions from clinicians, researchers, and professional software developers. The design of AnFiSA addresses the aforementioned issues via the following architectural principles: using a multidimensional database management system (DBMS) for genomic data to address reproducibility, curated decision trees adaptable to changing clinical rules, and a crowdsourcing-friendly interface to address difficult-to-diagnose cases. We discuss how we have chosen our technology stack and describe the design and implementation of the software. Finally, we show in detail how selected workflows can be implemented using the current version of AnFiSA by a medical geneticist.

Enhancing Cubes with Models to Describe Multidimensional Data.

The Intentional Analytics Model (IAM) has been recently envisioned as a new paradigm to couple OLAP and analytics. It relies on two basic ideas: (i) letting the user explore data by expressing her analysis intentions rather than the data she needs, and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of interesting model components (e.g., clusters). In this paper we contribute to give a proof-of-concept for the IAM vision by delivering an end-to-end implementation of describe, one of the five intention operators introduced by IAM. Among the research challenges left open in IAM, those we address are (i) automatically tuning the size of models (e.g., the number of clusters), (ii) devising a measure to estimate the interestingness of model components, (iii) selecting the most effective chart or graph for visualizing each enhanced cube depending on its features, and (iv) devising a visual metaphor to display enhanced cubes and interact with them. We assess the validity of our approach in terms of user effort for formulating intentions, effectiveness, efficiency, and scalability.

SafetyCube: Framework for potential pedestrian risk analysis using multi-dimensional OLAP.

In the past decade, the number of road traffic accidents and fatalities has remained about the same level. One of strategies to protect vulnerable road users (VRUs) is to analyze the factors that cause traffic accident and then to deploy safety facilities. However, most traffic safety systems currently in operation rely on historical data, which is post-facto approach. Thus, it is necessary to prevent accident in advance and to respond in proactive manner before the accident. In this study, we propose a framework for potential pedestrian risk analysis using a multi-dimensional on-line analytical processing (OLAP), called SafetyCube, which enables decision-makers to understand the situations by scrutinizing interactive behaviors between vehicle and pedestrian. First, we collect the behavioral features of traffic-related objects (e.g., vehicles and pedestrians) extracted from closed circuit televisions (CCTVs) deployed on crosswalks throughout the overall urban, and accumulate them in a data warehouse over an extended period in order to construct a data cube model. Then, we conduct comprehensive analyses in multi-dimensional perspective using OLAP operations by varying the abstraction levels. Our analytical experiments are based on three scenarios, and the results show that the vehicle's movement patterns before entering the crosswalk, patterns of changes in speed of vehicles approaching to pedestrians, and so on. Through these results from the proposed analytical system, decision-makers can gain a better understanding of how the vehicles and pedestrians behave near the crosswalk by visualizing their interactions. Further, these insights would be reflected to improve the road environment safer. In order to validate the feasibility and applicability of the proposed system, we apply it to various crosswalks in Osan city, South Korea.

Online Analytical Processing for Business Intelligence in Big Data.

Online analytical processing (OLAP) approach is widely used in business intelligence to cater the multidimensional queries for decades. In this era of cutting-edge technology and the internet, data generation rates have been rising exponentially. Internet of things sensors and social media platforms are some of the major contributors, leading toward the absolute data boom. Storage and speed are the crucial parameters and undoubtedly the burning issues in efficient data handling. The key idea here is to address these two challenges of big data computing in OLAP. In this article, the authors have proposed and implemented OLAP on Hadoop by Indexing (OOHI). OOHI offers a simplified multidimensional model that stores dimensions in the schema server and measures on the Hadoop cluster. Overall setup is divided into various modules, namely: data storage module (DSM), dimension encoding module (DEM), cube segmentation module, segment selection module (SSM), and block selection and process (BSAP) module. Serialization and deserialization concept applied by DSM for storage and retrieval of the data for efficient space utilization. Integer encoding adopted by DEM in dimension hierarchy is selected to escape sparsity problem in multidimensional big data. To reduce search space by chunks of the cube from the queried chunks, SSM plays an important role. Map reduce-based indexing approach and series of seek operations of BSAP module were integrated to achieve parallelism and fault tolerance. Real-time oceanography data and supermarket data sets are applied to demonstrate that OOHI model is data independent. Various test cases are designed to cover the scope of each dimension and volume of data set. Comparative results and performance analytics portray that OOHI outperforms in data storage, dice, slice, and roll-up operations compared with Hadoop based OLAP.

SPPOLAP: Computing Privacy-Preserving OLAP Data Cubes Effectively and Efficiently Algorithms, Complexity Analysis and Experimental Evaluation.

This paper provides significant contributions in the line of the so-called privacy-preserving OLAP research area, via extending the previous SPPOLAP's results provided recently. SPPOLAP is a state-of-the-art algorithm whose main goal consists in computing privacy-preserving OLAP data cubes effectively and efficiently. The main innovations carried-out by SPPOLAP are represented by the novel privacy OLAP notion and the flexible adoption of sampling-based techniques in order to achieve the final privacy-preserving data cube. In line with the main SPPOLAP's results, this paper significantly extends the previous research efforts by means of the following contributions: (i) complete algorithms of the whole SPPOLAP algorithmic framework; (ii) complexity analysis and results; (iii) comprehensive experimental analysis of SPPOLAP against real-life multidimensional data cubes, according to several experimental parameters. These contributions nice-fully complete the state-of-the-art SPPOLAP's results.

Querying large graphs in biomedicine with colored graphs and decomposition.

In graph networks, graph structural analytics such as betweenness centrality has played an important role in finding the most central vertices in graph data. Hence, betweenness centrality has been heavily applied to discover the most important genes with respect to multiple diseases in biomedicine research. Considering color as a property of graph data to represent different categories for the nodes and edges in the graph, we may investigate the betweenness centrality of each colored subgraph composed of a specific color. However, as investigators may be interested in querying betweenness centrality on multiple combinations of the colored subgraphs, the total execution time on all the subgraphs may be excessively long, considering all the possible combinations. In addition, the performance could be worse when the size of the graph grows larger. In this research, we propose an approach to computing betweenness centrality by incorporating node colors and edge colors. We propose that the node with the highest betweenness centrality can be computed for a very large and colored graph by decomposing the graph into colored subgraphs and merging the result from the base cases. Furthermore, we compare our approach with the conventional approaches in the experiments, and we demonstrate that our scalable approach is more efficient when finding the global backbone node with the highest betweenness centrality.

A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance.

Business analytics (BA) is increasingly being utilised by radiology departments to analyse and present data. It encompasses statistical analysis, forecasting and predictive modelling and is used as an umbrella term for decision support and business intelligence systems. The primary aim of this study was to determine whether utilising BA technologies could contribute towards improved decision support and resource management within radiology departments. A set of information technology requirements were identified with key stakeholders, and a prototype BA software tool was designed, developed and implemented. A qualitative evaluation of the tool was carried out through a series of semi-structured interviews with key stakeholders. Feedback was collated, and emergent themes were identified. The results indicated that BA software applications can provide visibility of radiology performance data across all time horizons. The study demonstrated that the tool could potentially assist with improving operational efficiencies and management of radiology resources.

An improved hybrid multi-criteria/multidimensional model for strategic industrial location selection: Casablanca industrial zones as a case study.

In this paper, we examine the issue of strategic industrial location selection in uncertain decision making environments for implanting new industrial corporation. In fact, the industrial location issue is typically considered as a crucial factor in business research field which is related to many calculations about natural resources, distributors, suppliers, customers, and most other things. Based on the integration of environmental, economic and social decisive elements of sustainable development, this paper presents a hybrid decision making model combining fuzzy multi-criteria analysis with analytical capabilities that OLAP systems can provide for successful and optimal industrial location selection. The proposed model mainly consists in three stages. In the first stage, a decision-making committee has been established to identify the evaluation criteria impacting the location selection process. In the second stage, we develop fuzzy AHP software based on the extent analysis method to assign the importance weights to the selected criteria, which allows us to model the linguistic vagueness, ambiguity, and incomplete knowledge. In the last stage, OLAP analysis integrated with multi-criteria analysis employs these weighted criteria as inputs to evaluate, rank and select the strategic industrial location for implanting new business corporation in the region of Casablanca, Morocco. Finally, a sensitivity analysis is performed to evaluate the impact of criteria weights and the preferences given by decision makers on the final rankings of strategic industrial locations.

A similarity-based data warehousing environment for medical images.

A core issue of the decision-making process in the medical field is to support the execution of analytical (OLAP) similarity queries over images in data warehousing environments. In this paper, we focus on this issue. We propose imageDWE, a non-conventional data warehousing environment that enables the storage of intrinsic features taken from medical images in a data warehouse and supports OLAP similarity queries over them. To comply with this goal, we introduce the concept of perceptual layer, which is an abstraction used to represent an image dataset according to a given feature descriptor in order to enable similarity search. Based on this concept, we propose the imageDW, an extended data warehouse with dimension tables specifically designed to support one or more perceptual layers. We also detail how to build an imageDW and how to load image data into it. Furthermore, we show how to process OLAP similarity queries composed of a conventional predicate and a similarity search predicate that encompasses the specification of one or more perceptual layers. Moreover, we introduce an index technique to improve the OLAP query processing over images. We carried out performance tests over a data warehouse environment that consolidated medical images from exams of several modalities. The results demonstrated the feasibility and efficiency of our proposed imageDWE to manage images and to process OLAP similarity queries. The results also demonstrated that the use of the proposed index technique guaranteed a great improvement in query processing.

Solutions for medical databases optimal exploitation.

The paper discusses the methods to apply OLAP techniques for multidimensional databases that leverage the existing, performance-enhancing technique, known as practical pre-aggregation, by making this technique relevant to a much wider range of medical applications, as a logistic support to the data warehousing techniques. The transformations have practically low computational complexity and they may be implemented using standard relational database technology. The paper also describes how to integrate the transformed hierarchies in current OLAP systems, transparently to the user and proposes a flexible, "multimodel" federated system for extending OLAP querying to external object databases.

A Two-Pass Exact Algorithm for Selection on Parallel Disk Systems.

Numerous OLAP queries process selection operations of "top N", median, "top 5%", in data warehousing applications. Selection is a well-studied problem that has numerous applications in the management of data and databases since, typically, any complex data query can be reduced to a series of basic operations such as sorting and selection. The parallel selection has also become an important fundamental operation, especially after parallel databases were introduced. In this paper, we present a deterministic algorithm Recursive Sampling Selection (RSS) to solve the exact out-of-core selection problem, which we show needs no more than (2 + ε) passes (ε being a very small fraction). We have compared our RSS algorithm with two other algorithms in the literature, namely, the Deterministic Sampling Selection and QuickSelect on the Parallel Disks Systems. Our analysis shows that DSS is a (2 + ε)-pass algorithm when the total number of input elements N is a polynomial in the memory size M (i.e., N = Mc for some constant c). While, our proposed algorithm RSS runs in (2 + ε) passes without any assumptions. Experimental results indicate that both RSS and DSS outperform QuickSelect on the Parallel Disks Systems. Especially, the proposed algorithm RSS is more scalable and robust to handle big data when the input size is far greater than the core memory size, including the case of N ≫ Mc .

Procedimiento para el desarrollo de un sistema de inteligencia de negocios en la gestión de ensayos clínicos en el Centro de Inmunología Molecular / Procedure for development of a business intelligence system in the clinical trials management at the Center of Molecular Immunology

La presente investigación surgió como parte de la colaboración que existe entre la Universidad de las Ciencias Informáticas y el Centro de Inmunología Molecular. El objetivo fue desarrollar un procedimiento que contribuyera al almacenamiento y análisis de los ensayos clínicos y que facilitara la aplicación integral de la inteligencia de negocios en esta actividad. Se realizó una propuesta de procedimiento para conducir el desarrollo de soluciones de inteligencia de negocios en el centro. El procedimiento fue evaluado a partir del método de experto Delphi y se obtuvo el resultado de Muy adecuado. Se contó además con un aval del centro cliente, donde se valoró de satisfactorio el trabajo realizado. La implementación de este procedimiento permitirá almacenar toda la información que se gestiona, de manera íntegra y estándar, con lo que se logrará viabilizar los análisis estadísticos que se necesitan realizar por parte de los especialistas de la institución

Present paper appeared as part of cooperation existing between the Information Sciences University and the Center of Molecular Immunology. The aim was to develop a procedure contributing to storage and analysis of clinical trials and to integral application of business intelligence service in this activity. A procedure proposal was made to manage the development of solutions in business intelligence in our center. Procedure was assessed from the method of the Delphi expert method obtaining a result qualified as very appropriate. Implementation of this procedure will allows storing all information managed in an integral and standard way, thus achieving to make viable the statistic analyses needed to be performed by the specialists of the institution

Usability Evaluation of the Spatial OLAP Visualization and Analysis Tool (SOVAT).

Increasingly sophisticated technologies, such as On-Line Analytical Processing (OLAP) and Geospatial Information Systems (GIS), are being leveraged for conducting community health assessments (CHA). Little is known about the usability of OLAP and GIS interfaces with respect to CHA. We conducted an iterative usability evaluation of the Spatial OLAP Visualization and Analysis Tool (SOVAT), a software application that combines OLAP and GIS. A total of nine graduate students and six community health researchers were asked to think-aloud while completing five CHA questions using SOVAT. The sessions were analyzed after every three participants and changes to the interface were made based on the findings. Measures included elapsed time, answers provided, erroneous actions, and satisfaction. Traditional OLAP interface features were poorly understood by participants and combined OLAP-GIS features needed to be better emphasized. The results suggest that the changes made to the SOVAT interface resulted in increases in both usability and user satisfaction.

A framework for designing a healthcare outcome data warehouse.

Many healthcare processes involve a series of patient visits or a series of outcomes. The modeling of outcomes associated with these types of healthcare processes is different from and not as well understood as the modeling of standard industry environments. For this reason, the typical multidimensional data warehouse designs that are frequently seen in other industries are often not a good match for data obtained from healthcare processes. Dimensional modeling is a data warehouse design technique that uses a data structure similar to the easily understood entity-relationship (ER) model but is sophisticated in that it supports high-performance data access. In the context of rehabilitation services, we implemented a slight variation of the dimensional modeling technique to make a data warehouse more appropriate for healthcare. One of the key aspects of designing a healthcare data warehouse is finding the right grain (scope) for different levels of analysis. We propose three levels of grain that enable the analysis of healthcare outcomes from highly summarized reports on episodes of care to fine-grained studies of progress from one treatment visit to the next. These grains allow the database to support multiple levels of analysis, which is imperative for healthcare decision making.