TEXT ANALYTICS AND DATA ACCESS AS SERVICES - A Case Study in Transforming a Legacy Client-server Text Analytics Workbench and Framework to SOA

2007 ◽  
Keyword(s):  
Data Access ◽  
Text Analytics ◽  
Client Server

Using Client/Server Technology to Enable Change: A Case Study

2017 ◽  
pp. 337-346
Author(s):  
Donald Saelens ◽  
Stuart Nelson
Keyword(s):  
Client Server

scholarly journals Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

2020 ◽  
Vol 1 ◽  
pp. 1-23
Author(s):  
Majid Hojati ◽  
Colin Robertson
Keyword(s):  
Big Data ◽  
Spatial Analysis ◽  
Cellular Automata ◽  
Spatial Data ◽  
Data Model ◽  
Data Access ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Global Grid

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

Improving Quality and Safety Through Use of Secondary Data: Methods Case Study

2016 ◽  
Vol 39 (11) ◽  
pp. 1477-1501 ◽  
Author(s):  
Victoria Goode ◽  
Nancy Crego ◽  
Michael P. Cary ◽  
Deirdre Thornlow ◽  
Elizabeth Merwin
Keyword(s):  
Research Question ◽  
Secondary Data ◽  
Data Access ◽  
Data Sets ◽  
Complex Data ◽  
Management Skills ◽  
Data Set ◽  
Large Numbers ◽  
Need To Evaluate

Researchers need to evaluate the strengths and weaknesses of data sets to choose a secondary data set to use for a health care study. This research method review informs the reader of the major issues necessary for investigators to consider while incorporating secondary data into their repertoire of potential research designs and shows the range of approaches the investigators may take to answer nursing research questions in a variety of context areas. The researcher requires expertise in locating and judging data sets and in the development of complex data management skills for managing large numbers of records. There are important considerations such as firm knowledge of the research question supported by the conceptual framework and the selection of appropriate databases, which guide the researcher in delineating the unit of analysis. Other more complex issues for researchers to consider when conducting secondary data research methods include data access, management and security, and complex variable construction.

Global Goes GUI: A Case Study of the Introduction of User-Centered Design

1995 ◽  
Vol 39 (12) ◽  
pp. 844-847
Author(s):  
Susan M. Dray
Keyword(s):  
Lessons Learned ◽  
User Centered Design ◽  
Client Server ◽  
Organizational Issues ◽  
Development Life Cycle ◽  
Server Systems ◽  
The Us ◽  
Early Phases ◽  
User Centered

Major changes in the design process are required for Information Systems departments to make the shift from a traditional development life cycle to the user-centered methods required for the development of Client/Server systems. This type of change can be very difficult to accomplish. “Global Enterprises,” a large commodities company, headquartered in the US, is in the early phases of this shift. Their strategy has been to form a cross-functional User Interface team. The efforts underway at Global are presented to illustrate many of the typical technical and organizational issues companies face early in the process of introducing new design methods. The paper concludes by summarizing on key lessons learned.

The modern seismic survey in the context of multi-client data access: A case study from Sabah, Malaysia

2019 ◽  
Author(s):  
Mazin Farouki ◽  
Jo McArdle ◽  
Andy Bromley ◽  
Shinya Sakamoto
Keyword(s):  
Data Access ◽  
Seismic Survey ◽  
Client Data

scholarly journals Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory

Micromachines ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 52
Author(s):  
Wenze Zhao ◽  
Yajuan Du ◽  
Mingzhe Zhang ◽  
Mingyang Liu ◽  
Kailun Jin ◽  
...  
Keyword(s):  
Random Access ◽  
Data Access ◽  
Database Systems ◽  
Data Migration ◽  
Memory Architecture ◽  
Hybrid Memory ◽  
High Bandwidth ◽  
Data Objects ◽  
Stacked Memory

With the advantage of faster data access than traditional disks, in-memory database systems, such as Redis and Memcached, have been widely applied in data centers and embedded systems. The performance of in-memory database greatly depends on the access speed of memory. With the requirement of high bandwidth and low energy, die-stacked memory (e.g., High Bandwidth Memory (HBM)) has been developed to extend the channel number and width. However, the capacity of die-stacked memory is limited due to the interposer challenge. Thus, hybrid memory system with traditional Dynamic Random Access Memory (DRAM) and die-stacked memory emerges. Existing works have proposed to place and manage data on hybrid memory architecture in the view of hardware. This paper considers to manage in-memory database data in hybrid memory in the view of application. We first perform a preliminary study on the hotness distribution of client requests on Redis. From the results, we observe that most requests happen on a small portion of data objects in in-memory database. Then, we propose the Application-oriented Data Migration called ADM to accelerate in-memory database on hybrid memory. We design a hotness management method and two migration policies to migrate data into or out of HBM. We take Redis under comprehensive benchmarks as a case study for the proposed method. Through the experimental results, it is verified that our proposed method can effectively gain performance improvement and reduce energy consumption compared with existing Redis database.

scholarly journals Iterative Guided Machine Learning-Assisted Systematic Literature Reviews: A Diabetes Case Study

2021 ◽  
Author(s):  
John Zimmerman ◽  
Robin Soler ◽  
Lavinder James ◽  
Murphy Sarah ◽  
Atkins Charisma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Literature ◽  
Relevant Information ◽  
Text Analytics ◽  
Inclusion Criteria ◽  
Literature Reviews ◽  
Study Test ◽  
Model Training

Abstract Background: Systematic Reviews (SR), studies of studies, use a formal process to evaluate the quality of scientific literature and determine ensuing effectiveness from qualifying articles to establish consensus findings around a hypothesis. Their value is increasing as the conduct and publication of research and evaluation has expanded and the process of identifying key insights becomes more time consuming. Text analytics and Machine Learning (ML) techniques may help overcome this problem of scale while still maintaining the level of rigor expected of SRs.Methods: In this article, we discuss an approach that uses existing examples of SRs to build and test a method for assisting the SR title and abstract pre-screening by reducing the initial pool of potential articles down to articles that meet inclusion criteria. Our approach differs from previous approaches to using ML as a SR tool in that it incorporates ML configurations guided by previously conducted SRs, and human confirmation on ML predictions of relevant articles during multiple iterative reviews on smaller tranches of citations. We applied the tailored method to a new SR review effort to validate performance. Results: the case study test of the approach proved a sensitivity (recall) in finding relevant articles during down selection that may rival many traditional processes. Conclusions: We believe this iterative method can help overcome bias in initial ML model training by having humans reinforce ML models with new and relevant information, and is an applied step towards transfer learning for ML in SR.

scholarly journals The Cambridge Cognitive and Psychiatric Assessment Kit (CamCOPS): A Secure Open-Source Client–Server System for Mobile Research and Clinical Data Capture

2021 ◽  
Vol 12 ◽  
Author(s):  
Rudolf N. Cardinal ◽  
Martin Burchell
Keyword(s):  
Open Source ◽  
Data Storage ◽  
Data Entry ◽  
Data Access ◽  
Data Capture ◽  
Third Party ◽  
Record System ◽  
Client Server ◽  
Network Connection ◽  
Server System

CamCOPS is a free, open-source client–server system for secure data capture in the domain of psychiatry, psychology, and the clinical neurosciences. The client is a cross-platform C++ application, suitable for mobile and offline (disconnected) use. It allows touchscreen data entry by subjects/patients, researchers/clinicians, or both together. It implements a large and extensible range of tasks, from simple questionnaires to complex animated tasks. The client uses encrypted data storage and sends data via an encrypted network connection to a CamCOPS server. Individual institutional users set up and run their own CamCOPS server, so no data is transferred outside the hosting institution's control. The server, written in Python, provides clinically oriented and research-oriented views of tasks, including the tracking of changes over time. It provides an audit trail, export facilities (such as to an institution's primary electronic health record system), and full structured data access subject to authorization. A single CamCOPS server can support multiple research/clinical groups, each having its own identity policy (e.g., fully identifiable for clinical use; de-identified/pseudonymised for research use). Intellectual property rules regarding third-party tasks vary and CamCOPS has several mechanisms to support compliance, including for tasks that may be permitted to some institutions but not others. CamCOPS supports task scheduling and home testing via a simplified user interface. We describe the software, report local information governance approvals within part of the UK National Health Service, and describe illustrative clinical and research uses.

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