Leveraging flexible data management with graph databases

2013 ◽  
Author(s):  
Elena Vasilyeva ◽  
Maik Thiele ◽  
Christof Bornhövd ◽  
Wolfgang Lehner
Keyword(s):  
Data Management ◽  
Graph Databases

Data, Storage and Index Models for Graph Databases

2011 ◽  
pp. 47-70 ◽  
Author(s):  
Srinath Srinivasa
Keyword(s):  
Data Management ◽  
Structural Properties ◽  
Data Storage ◽  
Structured Data ◽  
Data Sets ◽  
Graph Databases ◽  
Graph Data ◽  
Intractable Problems ◽  
Graph Data Management

Management of graph structured data has important applications in several areas. Queries on such data sets are based on structural properties of the graphs, in addition to values of attributes. Answering such queries pose significant challenges, as reasoning about structural properties across graphs are typically intractable problems. This chapter provides an overview of the challenges in designing databases over graph datasets. Different application areas that use graph databases, pose their own unique set of challenges, making the task of designing a generic graph-oriented DBMS still an elusive goal. The purpose of this chapter is to provide a tutorial introduction to some of the major challenges of graph data management, survey some of the piecemeal solutions that have been proposed, and suggest an overall structure in which these different solutions can be meaningfully placed.

scholarly journals Neo4j Graph Database Implementation for LinkedIn

2020 ◽  
pp. 339-342
Author(s):  
Punam Nikam ◽  
Sachin Bhoite ◽  
Anuj Shenoy
Keyword(s):  
Social Media ◽  
Data Management ◽  
Graph Database ◽  
Graph Databases ◽  
Database Implementation

The presented article mainly circumspect the idea for the use and implementation of graph database in the most social media of today. Currently many companies are using neo4j graph database for their workouts for data management. We shall stick with social media for this particular paper. We have used neo4j graph database for maintaining data of LinkedIn user and pages in a very systematic manner. Facebook and Twitter do currently use graph databases so we thought of implementing the same for LinkedIn.

Health Informatics: Handle with Caution

1997 ◽  
Vol 36 (02) ◽  
pp. 79-81
Author(s):  
V. Leroy ◽  
S. Maurice-Tison ◽  
B. Le Blanc ◽  
R. Salamon
Keyword(s):  
Health Care ◽  
Data Management ◽  
Medical Informatics ◽  
Health Care Professionals ◽  
Data Security ◽  
Health Informatics ◽  
The Other ◽  
Inappropriate Use

Abstract:The increased use of computers is a response to the considerable growth in information in all fields of activities. Related to this, in the field of medicine a new component appeared about 40 years ago: Medical Informatics. Its goals are to assist health care professionals in the choice of data to manage and in the choice of applications of such data. These possibilities for data management must be well understood and, related to this, two major dangers must be emphasized. One concerns data security, and the other concerns the processing of these data. This paper discusses these items and warns of the inappropriate use of medical informatics.

Data Management in an Epidemiological Study : Experiences from the Iowa 500 Field Follow-up and Family Study

1980 ◽  
Vol 19 (01) ◽  
pp. 37-41
Author(s):  
R. F. Woolson ◽  
M. T. Tsuang ◽  
L. R. Urban
Keyword(s):  
Data Management ◽  
Epidemiological Study ◽  
Family Study ◽  
Data Sets ◽  
Data Handling ◽  
Management Decisions ◽  
Numerous Data ◽  
Made In

We are now conducting a forty-year follow-up and family study of 200 schizophrenics, 325 manic-depressives and 160 surgical controls. This study began in 1973 and has continued to the present date. Numerous data handling and data management decisions were made in the course of collecting the data for the project. In this report some of the practical difficulties in the data handling and computer management of such large and bulky data sets are enumerated.

Framework for Biosignal Interpretation in Intensive Care and Anesthesia

1997 ◽  
Vol 36 (04/05) ◽  
pp. 340-344 ◽  
Author(s):  
I. Korhonen ◽  
M. van Gils ◽  
A. Kari ◽  
N. Saranummi
Keyword(s):  
Critical Care ◽  
Intensive Care ◽  
Data Management ◽  
Clinical Evaluation ◽  
Patient Monitoring ◽  
Research Community ◽  
Clinical Routine ◽  
The Poor ◽  
Outcomes Of Care

Abstract:Improved monitoring improves outcomes of care. As critical care is “critical”, everything that can be done to detect and prevent complications as early as possible benefits the patients. In spite of major efforts by the research community to develop and apply sophisticated biosignal interpretation methods (BSI), the uptake of the results by industry has been poor. Consequently, the BSI methods used in clinical routine are fairly simple. This paper postulates that the main reason for the poor uptake is the insufficient bridging between the actors (i.e., clinicians, industry and research). This makes it difficult for the BSI developers to understand what can be implemented into commercial systems and what will be accepted by clinicians as routine tools. A framework is suggested that enables improved interaction and cooperation between the actors. This framework is based on the emerging commercial patient monitoring and data management platforms which can be shared and utilized by all concerned, from research to development and finally to clinical evaluation.

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