An Eligibility Criteria Query Language for Heterogeneous Data Warehouses

2015 ◽  
Vol 54 (01) ◽  
pp. 41-44 ◽  
Author(s):  
A. Taweel ◽  
S. Miles ◽  
B. C. Delaney ◽  
R. Bache
Keyword(s):  
Clinical Data ◽  
Query Language ◽  
Data Representation ◽  
Query Languages ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Data Warehouses ◽  
Eligibility Criteria ◽  
Strong Basis ◽  
Temporal Semantics

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Objectives: The increasing availability of electronic clinical data provides great potential for finding eligible patients for clinical research. However, data heterogeneity makes it difficult for clinical researchers to interrogate sources consistently. Existing standard query languages are often not sufficient to query across diverse representations. Thus, a higher- level domain language is needed so that queries become data-representation agnostic. To this end, we define a clinician-readable computational language for querying whether patients meet eligibility criteria (ECs) from clinical trials. This language is capable of implementing the temporal semantics required by many ECs, and can be automatically evaluated on heterogeneous data sources.Methods: By reference to standards and examples of existing ECs, a clinician-readable query language was developed. Using a model-based approach, it was implemented to transform captured ECs into queries that interrogate heterogeneous data warehouses. The query language was evaluated on two types of data sources, each different in structure and content.Results: The query language abstracts the level of expressivity so that researchers construct their ECs with no prior knowledge of the data sources. It was evaluated on two types of semantically and structurally diverse data warehouses. This query language is now used to express ECs in the EHR4CR project. A survey shows that it was perceived by the majority of users to be useful, easy to understand and unambiguous.Discussion: An EC-specific language enables clinical researchers to express their ECs as a query such that the user is isolated from complexities of different heterogeneous clinical data sets. More generally, the approach demonstrates that a domain query language has potential for overcoming the problems of semantic interoperability and is applicable where the nature of the queries is well understood and the data is conceptually similar but in different representations.Conclusions: Our language provides a strong basis for use across different clinical domains for expressing ECs by overcoming the heterogeneous nature of electronic clinical data whilst maintaining semantic consistency. It is readily comprehensible by target users. This demonstrates that a domain query language can be both usable and interoperable.

Web Retrieval of XML Documents

2011 ◽  
pp. 170-199
Author(s):  
Barbara Catania ◽  
Elena Ferrari
Keyword(s):  
Expressive Power ◽  
Data Representation ◽  
Query Languages ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Xml Data ◽  
Web Documents ◽  
Web Retrieval ◽  
Heterogeneous Data Sources ◽  
The Web

Web is characterized by a huge amount of very heterogeneous data sources, that differ both in media support and format representation. In this scenario, there is the need of an integrating approach for querying heterogeneous Web documents. To this purpose, XML can play an important role since it is becoming a standard for data representation and exchange over the Web. Due to its flexibility, XML is currently being used as an interface language over the Web, by which (part of) document sources are represented and exported. Under this assumption, the problem of querying heterogeneous sources can be reduced to the problem of querying XML data sources. In this chapter, we first survey the most relevant query languages for XML data proposed both by the scientific community and by standardization committees, e.g., W3C, mainly focusing on their expressive power. Then, we investigate how typical Information Retrieval concepts, such as ranking, similarity-based search, and profile-based search, can be applied to XML query languages. Commercial products based on the considered approaches are then briefly surveyed. Finally, we conclude the chapter by providing an overview of the most promising research trends in the fields.

scholarly journals CLINICAL DATA WAREHOUSE: A REVIEW

2018 ◽  
Vol 44 (2) ◽  
pp. 16-26 ◽  
Author(s):  
Alaa Hamoud ◽  
Ali Hashim ◽  
Wid Awadh
Keyword(s):  
Data Warehouse ◽  
Clinical Data ◽  
Clinical Information ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Security And Privacy ◽  
Clinical Environment ◽  
Privacy Concerns ◽  
Clinical Data Warehouse ◽  
Heterogeneous Data Sources

Clinical decisions are crucial because they are related to human lives. Thus, managers and decision makers inthe clinical environment seek new solutions that can support their decisions. A clinical data warehouse (CDW) is animportant solution that is used to achieve clinical stakeholders’ goals by merging heterogeneous data sources in a centralrepository and using this repository to find answers related to the strategic clinical domain, thereby supporting clinicaldecisions. CDW implementation faces numerous obstacles, starting with the data sources and ending with the tools thatview the clinical information. This paper presents a systematic overview of purpose of CDWs as well as the characteristics;requirements; data sources; extract, transform and load (ETL) process; security and privacy concerns; design approach;architecture; and challenges and difficulties related to implementing a successful CDW. PubMed and Google Scholarare used to find papers related to CDW. Among the total of 784 papers, only 42 are included in the literature review. Thesepapers are classified based on five perspectives, namely methodology, data, system, ETL tool and purpose, to findinsights related to aspects of CDW. This review can contribute answers to questions related to CDW and providerecommendations for implementing a successful CDW.

scholarly journals Leaf: an open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research

2019 ◽  
Vol 27 (1) ◽  
pp. 109-118 ◽  
Author(s):  
Nicholas J Dobbins ◽  
Clifford H Spital ◽  
Robert A Black ◽  
Jason M Morrison ◽  
Bas de Veer ◽  
...  
Keyword(s):  
Clinical Data ◽  
Web Application ◽  
Building Blocks ◽  
Heterogeneous Data ◽  
Data Models ◽  
Direct Access ◽  
Data Warehouses ◽  
Concept System ◽  
Clinical Databases ◽  
Large Patient

Abstract Objective Academic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of clinical data. Enterprise data warehouses have emerged as central resources for these data, but often require an informatician to extract meaningful information, limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying clinical data from heterogeneous data models and sources. Materials and Methods Leaf utilizes a flexible biomedical concept system to define hierarchical concepts and ontologies. Each Leaf concept contains both textual representations and SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries. Results Leaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an enterprise data warehouse with over 300 active users. Through the support of UW Medicine (https://uwmedicine.org), the Institute of Translational Health Sciences (https://www.iths.org), and the National Center for Data to Health (https://ctsa.ncats.nih.gov/cd2h/), Leaf source code has been released into the public domain at https://github.com/uwrit/leaf. Discussion Leaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication. Conclusions Leaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly leverage existing user authentication systems and clinical databases in situ. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine biobanks, and clinical studies involving large patient cohorts.

Integrating Heterogeneous Data Sources in the Web

2009 ◽  
pp. 2472-2488
Author(s):  
Angelo Brayner ◽  
Marcelo Meirelles ◽  
José de Aguiar Moraes Filho
Keyword(s):  
Query Language ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Distributed Data ◽  
Local Data ◽  
Multidatabase System ◽  
Integration Strategy ◽  
Heterogeneous Data Sources ◽  
Integration Problems ◽  
The Web

Integrating data sources published on the Web requires an integration strategy that guarantees the local data sources’ autonomy. A multidatabase system (MDBS) has been consolidated as an approach to integrate multiple heterogeneous and distributed data sources in flexible and dynamic environments such as the Web. A key property of MDBSs is to guarantee a higher degree of local autonomy. In order to adopt the MDBS strategy, it is necessary to use a query language, called the MultiDatabase Language (MDL), which provides the necessary constructs for jointly manipulating and accessing data in heterogeneous data sources. In other words, the MDL is responsible for solving integration conflicts. This chapter describes an extension to the XQuery Language, called MXQuery, which supports queries over several data sources and solves such integration problems as semantic heterogeneity and incomplete information.

Cube Algebra

2013 ◽  
Vol 9 (2) ◽  
pp. 39-65 ◽  
Author(s):  
Cristina Ciferri ◽  
Ricardo Ciferri ◽  
Leticia Gómez ◽  
Markus Schneider ◽  
Alejandro Vaisman ◽  
...  
Keyword(s):  
Conceptual Model ◽  
User Interfaces ◽  
Query Language ◽  
Knowledge Worker ◽  
Query Languages ◽  
Data Warehouses ◽  
End User ◽  
Uml Extensions ◽  
High Level ◽  
User Friendly

The lack of an appropriate conceptual model for data warehouses and OLAP systems has led to the tendency to deploy logical models (for example, star, snowflake, and constellation schemas) for them as conceptual models. ER model extensions, UML extensions, special graphical user interfaces, and dashboards have been proposed as conceptual approaches. However, they introduce their own problems, are somehow complex and difficult to understand, and are not always user-friendly. They also require a high learning curve, and most of them address only structural design, not considering associated operations. Therefore, they are not really an improvement and, in the end, only represent a reflection of the logical model. The essential drawback of offering this system-centric view as a user concept is that knowledge workers are confronted with the full and overwhelming complexity of these systems as well as complicated and user-unfriendly query languages such as SQL OLAP and MDX. In this article, the authors propose a user-centric conceptual model for data warehouses and OLAP systems, called the Cube Algebra. It takes the cube metaphor literally and provides the knowledge worker with high-level cube objects and related concepts. A novel query language leverages well known high-level operations such as roll-up, drill-down, slice, and drill-across. As a result, the logical and physical levels are hidden from the unskilled end user.

Temporal Semistructured Data Models and Data Warehouses

2011 ◽  
pp. 277-297 ◽  
Author(s):  
Carlo Combi ◽  
Barbara Oliboni
Keyword(s):  
Data Warehouse ◽  
Data Model ◽  
Heterogeneous Data ◽  
Data Models ◽  
Semistructured Data ◽  
Data Sources ◽  
Time Varying ◽  
Data Warehouses ◽  
Time Dimension ◽  
Heterogeneous Data Sources

This chapter describes a graph-based approach to represent information stored in a data warehouse, by means of a temporal semistructured data model. We consider issues related to the representation of semistructured data warehouses, and discuss the set of constraints needed to manage in a correct way the warehouse time, i.e. the time dimension considered storing data in the data warehouse itself. We use a temporal semistructured data model because a data warehouse can contain data coming from different and heterogeneous data sources. This means that data stored in a data warehouse are semistructured in nature, i.e. in different documents the same information can be represented in different ways, and moreover, the document schemata can be available or not. Moreover, information stored into a data warehouse is often time varying, thus as for semistructured data, also in the data warehouse context, it could be useful to consider time.

Integrating Heterogeneous Data Sources in the Web

2007 ◽  
pp. 199-219
Author(s):  
Angelo Brayner ◽  
Macelo Meireles ◽  
José de Aguiar Moraes Filho
Keyword(s):  
Query Language ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Distributed Data ◽  
Local Data ◽  
Multidatabase System ◽  
Integration Strategy ◽  
Heterogeneous Data Sources ◽  
Integration Problems ◽  
The Web

Integrating data sources published on the web requires an integration strategy that guarantees local data sources autonomy. Multidatabase System (MDBS) has been consolidated as an approach to integrate multiple heterogeneous and distributed data sources in flexible and dynamic environments such as the Web. A key property of MDBSs is to guarantee a higher degree of local autonomy. In order to adopt the MDBS strategy, it is necessary to use a query language, called multidatabase language (MDL), which provides the necessary constructs for jointly manipulating and accessing data in heterogeneous data sources. In other words, the MDL is responsible for solving integration conflicts. This chapter describes an extension to the XQuery language, called MXQuery, which supports queries over several data sources and solves integration problems as semantic heterogeneity and incomplete information.

scholarly journals Characterizing Tractability of Simple Well-Designed Pattern Trees with Projection

2020 ◽  
Author(s):  
Stefan Mengel ◽  
Sebastian Skritek
Keyword(s):  
Semantic Web ◽  
Structural Characterization ◽  
Incomplete Data ◽  
Query Language ◽  
Query Languages ◽  
Data Sources ◽  
Central Feature ◽  
Simple Pattern ◽  
Underlying Graph

Abstract We study the complexity of evaluating well-designed pattern trees, a query language extending conjunctive queries with the possibility to define parts of the query to be optional. This possibility of optional parts is important for obtaining meaningful results over incomplete data sources as it is common in semantic web settings. Recently, a structural characterization of the classes of well-designed pattern trees that can be evaluated in polynomial time was shown. However, projection—a central feature of many query languages—was not considered in this study. We work towards closing this gap by giving a characterization of all tractable classes of simple well-designed pattern trees with projection (under some common complexity theoretic assumptions). Since well-designed pattern trees correspond to the fragment of well-designed {, }-SPARQL queries this gives a complete description of the tractable classes of queries with projections in this fragment that can be characterized by the underlying graph structures of the queries. For non-simple pattern trees the tractability criteria for simple pattern trees do not capture all tractable classes. We thus extend the characterization for the non-simple case in order to capture some additional tractable cases.

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