Web Services Based Integration Tool for Heterogeneous Databases

A Web-Based Perioperative Dashboard as a Platform for Anesthesia Informatics Innovation.

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

Patient cohort identification across heterogeneous data sources is a challenging task, which may involve a complicated process of data loading, harmonization and querying. Most existing cohort identification tools use a relational database model implemented in SQL for storing patient data. However, SQL databases have restrictions on the maximum number of columns in a table, which necessitates the breaking down of high dimensional data into multiple tables and as a consequence affects query performance. In this paper, we developed two NoSQL-based patient cohort query systems based on an existing SQL-based system for the cross-cohort query in the National Sleep Resource Research (NSRR). We used eight NSRR datasets in our experiment to evaluate the performance of the NoSQLbased and SQL-based systems in data loading, harmonization and query. Our experiment showed that NoSQL-based approaches outperformed the SQL-based and are rather promising for developing patient cohort query systems across heterogeneous data sources.

Data Warehouses (DWs) are currently considered to be the cornerstone of Business Intelligence (BI) systems. Security is a key issue in DWs since the business information that they manage is crucial and highly sensitive, and should be carefully protected. However, the increasing amount of data available on the Web signifies that more and more DW systems are considering the Web as the primary data source through which to populate their DWs. XML is therefore widely accepted as being the principal means through which to provide easier data and metadata interchange among heterogeneous data sources from the Web and the DW systems.Although security issues have been considered during the whole development process of traditional DWs, current research lacks approaches with which to consider security when the target platform is based on the Web and XML technologies. The idiosyncrasy of the unstructured and semi-structured data available on the Web definitely requires particular security rules that are specifically tailored to these systems in order to permit their particularities to be captured correctly.In order to tackle this situation, in this paper, we propose a methodological approach based on the Model Driven Architecture (MDA) for the development of Secure XML DWs. We therefore specify a set of transformation rules that are able to automatically generate not only the corresponding XML structure of the DW from secure conceptual DW models, but also the security rules specified within the DW XML structure, thus allowing us to implement both aspects simultaneously. A case study is provided at the end of the paper to show the benefits of our approach.

Web based environments will typically need to maintain many documents (e.g. simulation model information and data results from multiple sites). Ideally, this information would be held in a widely utilized and highly viewable format. The eXtensible Markup Language (XML) is a new standard that meets this criteria for World Wide Web based applications. It is sophisticated enough so that complex real world structures and relationships may be captured. Large collections of XML documents can be stored for efficient retrieval using database management systems (DBMSs). Current database management is represented by the object oriented (OO) and object-relational (OR) database systems. Each type has their own query languages, Object Query Language (OQL, v.2) and Structured Query Language (SQL), respectively. These database query languages provide a high-level way to ask for information from a database. The article looks at storing and retrieving XML documents. It surveys work being done on query languages and tools for XML. It then discusses a simple graphical XML query tool for front end use in the Java Simulation (JSIM) Web based simulation environment.

Traditionally, searching XML data with a structured query often identifies the exact matches for the query and returns them as the qualified results. However, the structural heterogeneity of the large number of XML data sources will make it difficult to answer the structured queries exactly. As such, query relaxation is necessary when the exact results do not exist or the number of the exact results is not enough. Previous work on XML query relaxation poses the problem of unnecessary computation of a big number of unqualified relaxed queries. This thesis addresses several fundamental issues in ranking a set of heterogeneous XML data sources, designing the adaptive relaxation rules and efficiently processing the relaxed queries over the data sources. Relaxing the specified queries may loose the constraints of the users' original preferences, which can increase the number of relevant results significantly. Therefore, in this work the users' queries would be answered with a ranked list of the best matched results, e.g., top-k problem means that only the top k relevant results are interesting to users. In addition, at each time we prefer to evaluate the relaxed queries over the data source that is the most relevant to the specified queries, and incrementally output the retrieved results that are guaranteed to have the higher relevance to the queries than the candidates in the other data sources, thus minimizing query processing time. However, query relaxation would become time-consuming and ineffective in some cases, such as the data structure is too complex for users to write structured queries, or the users do not know structured query languages. To address this problem, we allow users to issue keyword queries. Different from previous keyword search methods, we first construct structured query templates based on the given keyword query and the underlying source schemas and then evaluate the query templates to answer the original keyword query based on our proposed ranking model.

A data warehouse (or smaller-scale data mart) is a specially prepared repository of data created to support decision making. Data are extracted from source systems, cleaned/scrubbed, transformed, and placed in data stores (Gorla, 2003). A data warehouse has data suppliers who are responsible for delivering data to the ultimate end users of the warehouse, such as analysts, operational personnel, and managers. The data suppliers make data available to end users either through structured query language (SQL) queries or custom-built decision-support applications, including decision support systems (DSS) and executive information systems (EIS).

Chapter 2 - Querying

In the past few years, several conceptual approaches have been proposed for the specification of the main multidimensional (MD) properties of the data warehouse (DW) repository. However, most of them deal with isolated aspects of the DW and do not provide designers with an integrated and standard method for designing the whole DW life cycle (ETL processes, data sources, DW repository and so on).Some approaches are depending on specific platform or neglecting important issues in DW design life cycle. Extraction-transformation-loading (ETL) processes play an important role in data warehouse architecture because they are responsible of integrating data from heterogeneous data sources into the DW repository. This paper proposes a conceptual model to refresh data warehouse by (insert, update, delete) data using ETL processes and considering DW security requirements. Firstly, the proposed ETL model is based on Unified Modeling Language (UML), which allows us to accomplish the conceptual modeling of ETL processes .secondly; this part focuses on how to integrate the proposed ETL model with the DW model. The proposed DW model depends on the Model Driven Architecture (MDA). MDA is a standard framework for software development that addresses the complete life cycle of designing, deploying, integrating, and managing applications by using models in software development. This paper proposes an integrated conceptual model for addressing temporal data warehouse security requirements (CMTDWS).

This paper applies web services using the common information model (CIM) to exchange measurement data from the transformer terminal unit (TTU) to the distribution management system (ADMS) for the aging estimation of distribution transformers. An application programming interface (API) is designed by using web services such as simple object access protocol (SOAP) and representational state transfer (RESTful) for TTU measurement data exchange. The structured query language (SQL) and the not only SQL (NoSQL) databases are used to store historical TTU data for neural network (NN) model training. An optimization architecture of web service with the database is proposed to increase data exchange performance. The exchanged real-time TTU data is input into a trained NN model that can provide a reference aging factor for the utility crew to balance distribution transformer loading. Finally, the graphic user interface (GUI) is designed in this paper to display real-time information and achieve asset management of distribution transformers.

Data warehouses collect and maintain large amounts of data from several distributed and heterogeneous data sources. Because of security reasons, operational requirements, and technical feasibility it is often impossible for data warehouses to access the data sources directly. Instead data warehouses have to replicate legacy information as detail data in order to be able to maintain their summary data.In this paper we investigate how to minimize the amount of detail data stored in a data warehouse. More specifically, we identify the minimal amount of data that has to be replicated in order to maintain, either incrementally or by recomputation, summary data defined in terms of generalized project-select-join (GPSJ) views. We show how to minimize the number of tuples and attributes in the current detail tables and even aggregate them where possible. The amount of data to be stored in current detail tables is minimized by exploiting smart duplicate compression in addition to local and join reductions. We identify situations where it becomes possible to omit the typically huge fact table and prove that these techniques in concert ensure that the current detail data is minimal in the sense that no subset of it permits to accurately maintain the same summary data. Finally, we sketch how existing maintenance methods can be adapted to use the minimal detail tables we propose.KeywordsFact TableLocal ReductionBase TableHeterogeneous Data SourceStar SchemaThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Transact-SQL (T-SQL) is the dialect of the Structured Query Language (SQL) adopted by Microsoft’s SQL Server database engine. T-SQL has been around since the early days of the Sybase code in the early 1990s. It has evolved throughout the years and today is a robust programming language based on the ANSI SQL: 2008 standard. In this chapter, you will learn how to access data using T-SQL statements as well as how to use many of the programming capabilities within the database engine, including stored procedures and functions.

In today's IT infrastructures, data is stored in SQL databases, non-SQL, databases, and host databases like ISAM/VSAM files. Non-SQL databases are specialized data stores controlled by applications like spreadsheets, mail, directory and index services. Developing applications accessing a variety of different data sources is challenging for application developers due to different environments, APIs, bindings, etc. 20 years ago, SQL was created to ease the life of database application developers and provide a uniform way for accessing data which is stored in SQL databases. The paper describes an implementation of table functions and its usage for accessing data stored outside SQL databases in diverse external data stores. Table functions are compliant with the relational data model, and therefore fit into the well established SQL language. The table-function architecture is open, and allows the deployment of generic data access infrastructures such as Microsoft's OLE DB or Java's JDBC (G. Hamilton et al., 1997). The paper describes a prototype implementation of OLE DB table functions with advanced query optimization techniques. The prototype is based on IBM DB2 UDB relational database system.

Editorial

The two driving forces for designing dimensional models of data warehouse are data sources and business requirements. Medical data sources are characterized by too many data types, large amount of data, lack of associations among data, mixed dictionary tables, etc., which result in lacking uniform representation and effective organization among a large number of concepts. These features may lead to serious semantic heterogeneity among different data sources. Medical data warehouse design involves a wide range of business requirements, while the users' perspectives are different and the expression of semantic heterogeneity is substantial. So how to obtain and integrate these business requirements is a challenge. Aiming at the characteristics of medical data sources and business requirements, this paper proposes an ontology-based medical data warehouse dimensional modeling method. Experiment shows that this method can not only optimize the data warehouse requirements analysis process, but also effectively eliminate the semantic heterogeneity in data sources and business requirements, thereby greatly improving the efficiency of medical data warehouse dimensional modeling.