scholarly journals In the Future, Data and Code Should Be FAIR

Cell Systems ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 95 ◽  
Author(s):  
Quincey Justman
Keyword(s):  
The Future ◽  
Future Data

A Study of XML Models for Data Mining

Data Mining ◽  
2013 ◽  
pp. 1-27
Author(s):  
Sangeetha Kutty ◽  
Richi Nayak ◽  
Tien Tran
Keyword(s):  
Data Mining ◽  
Data Model ◽  
Data Representation ◽  
Data Models ◽  
Data Mining Techniques ◽  
Xml Documents ◽  
The Future ◽  
Future Data

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.

The MIL-STD-1553B data bus: What does the future hold?

2007 ◽  
Vol 111 (1118) ◽  
pp. 231-246 ◽  
Author(s):  
D. R. Bracknell
Keyword(s):  
Cost Effective ◽  
Data Networks ◽  
Fibre Channel ◽  
Natural Choice ◽  
Operational Life ◽  
The Future ◽  
Future Data ◽  
Data Bus ◽  
Commercial Off The Shelf ◽  
Electro Magnetic

Numerous military platforms (land, sea and air) feature serial data bus technology based on the US MIL-STD-1553B data bus standard for integration of their digital systems. Many of these platforms have 15-20 years of operational life remaining, but the installed 1553B data buses (data networks) having only a 1Mbit/sec transfer rate are unable to meet many of the future data networking requirements. Research into new, higher performance data networks has concentrated on modern alternatives with throughput increases of two to three orders of magnitude (100Mbit/sec to 1Gbit/sec). These are generally based on modern commercial-off-the-shelf (COTS) standards, good examples being Ethernet and Fibre Channel. Some are already being employed in military platforms having been ruggedised for the harsh physical and electro-magnetic environment. However these COTS systems while being a natural choice for new platforms may not be cost effective for upgrading older platforms. This paper plots the history of MIL-STD-1553, possibly the most successful military platform standard of all time, and discusses some of the options for increasing its performance and economically extending its life into the future.

Exploring for the Future: data release of whole-rock Re–Os from the South Nicholson region, Northern Territory and Queensland

2020 ◽  
Author(s):  
J.R. Anderson ◽  
A.J.M. Jarrett ◽  
C.J. Boreham ◽  
D.C. Champion ◽  
D. Huston ◽  
...  
Keyword(s):  
Northern Territory ◽  
The South ◽  
Data Release ◽  
The Future ◽  
Future Data

STATUS OF THE KLOE EXPERIMENT AT DAΦNE

2002 ◽  
Vol 17 (22) ◽  
pp. 3026-3035
Author(s):  
◽  
FABIO BOSSI
Keyword(s):  
Radiative Decays ◽  
The Future ◽  
First Results ◽  
Future Data ◽  
Year 2000

Since April 1999, the KLOE experiment at DAΦNE has collected about 200 pb-1 of data, produced in e+ - e- collision at the c.m. energy of 1020 MeV, the mass of the ϕ(1020) meson. This data has been used for detailed studies on the ϕ radiative decays, as well as on rare [Formula: see text] decays. The first results, based on the ~ 20 pb-1 collected in year 2000 are presented here. Perspectives for the future data taking are also discussed.

AgCleaning: A Track Data Filling Algorithm Based on Movement Recency for RFID Track Data

2014 ◽  
Vol 490-491 ◽  
pp. 1330-1337
Author(s):  
Kong Fa Hu ◽  
Long Li ◽  
Zhi Peng Lu
Keyword(s):  
Historical Data ◽  
Data Cleaning ◽  
Space Time ◽  
Time Factor ◽  
Event Tree ◽  
The Future ◽  
Future Data ◽  
Movement Rules ◽  
Data Level

For the traditional data cleaning algorithms mainly fill up the data based on the space-time relevance in the data level, they are not suitable for RFID application scenarios with track information based on multi-logical areas. This paper proposed a track data filling algorithm based on movement recency by studying the characteristics of RFID track data. This algorithm maintains a track event tree according to the historical data, to predict the future data and guide the data cleaning. Also it considers the effect on the movement rules from time factor and brings in the ageing factor for maintaining the track event tree, which improved the predict accuracy of the tree and raise the veracity of the filling algorithm.

scholarly journals 5V‟s of Big Data Attributes and their Relevance and Importance across Domains

2020 ◽  
Vol 9 (11) ◽  
pp. 154-163
Keyword(s):  
Big Data ◽  
Data System ◽  
Data Systems ◽  
Wholesale Trade ◽  
Formal Study ◽  
The Future ◽  
Future Data ◽  
Immense Potential ◽  
Robust Systems

Data is an ocean of Universal Facts”. Big data once an emergent technology of study is in its prime with immense potential for future technological advancements. A formal study in the attributes of data is essential to build robust systems of the future. Data scientists need a basic foot hold when studying data systems and their applications in various domains. This paper intends to be THE go-to resource for every student and professional desirous to make an entry in the field of Big Data. This paper has two focus areas. The first area of focus is the detailing of the 5 V attributes of data i.e. Volume, Variety, Velocity, Veracity and Value. Secondly, we will endeavor to present a domain wise independent as well as comparative of the correlation between the 5 V’s of Big Data. We have researched and collected information from various market watch dogs and concluded by carrying out comparatives which are highlighted in this publication. The domains we will mention are Wholesale Trade Domain, Retail Domain, Utilities Domain, Education Domain, Transportation Domain, Banking and Securities Domain, Communication and Media Domain, Manufacturing Domain, Government Domain, Healthcare Domain, etc. This is invaluable information for Big Data system designers as well as future researchers.

scholarly journals Session centered Recommendation Utilizing Future Contexts in Social Media

2021 ◽  
Vol 29 (3) ◽  
pp. 91-104
Author(s):  
Sanjeev Dhawan ◽  
Kulvinder Singh ◽  
Adrian Rabaea ◽  
Amit Batra
Keyword(s):  
Data Augmentation ◽  
Empirical Studies ◽  
Sequential Data ◽  
Space Filling ◽  
The Future ◽  
Future Data ◽  
Real World Datasets ◽  
Sequential Information ◽  
Better Than

Abstract Session centered recommender systems has emerged as an interesting and challenging topic amid researchers during the past few years. In order to make a prediction in the sequential data, prevailing approaches utilize either left to right design autoregressive or data augmentation methods. As these approaches are used to utilize the sequential information pertaining to user conduct, the information about the future context of an objective interaction is totally ignored while making prediction. As a matter of fact, we claim that during the course of training, the future data after the objective interaction are present and this supplies indispensable signal on preferences of users and if utilized can increase the quality of recommendation. It is a subtle task to incorporate future contexts into the process of training, as the rules of machine learning are not followed and can result in loss of data. Therefore, in order to solve this problem, we suggest a novel encoder decoder prototype termed as space filling centered Recommender (SRec), which is used to train the encoder and decoder utilizing space filling approach. Particularly, an incomplete sequence is taken into consideration by the encoder as input (few items are absent) and then decoder is used to predict these items which are absent initially based on the encoded interpretation. The general SRec prototype is instantiated by us employing convolutional neural network (CNN) by giving emphasis on both e ciency and accuracy. The empirical studies and investigation on two real world datasets are conducted by us including short, medium and long sequences, which exhibits that SRec performs better than traditional sequential recommendation approaches.

A Study of XML Models for Data Mining

2011 ◽  
pp. 1-28
Author(s):  
Sangeetha Kutty ◽  
Richi Nayak ◽  
Tien Tran
Keyword(s):  
Data Mining ◽  
Data Model ◽  
Data Representation ◽  
Data Models ◽  
Data Mining Techniques ◽  
Xml Documents ◽  
The Future ◽  
Future Data

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.

The SHARE program for dementia: Implementation of an early-stage dyadic care-planning intervention

Dementia ◽  
2016 ◽  
Vol 18 (1) ◽  
pp. 360-379 ◽  
Author(s):  
Silvia Orsulic-Jeras ◽  
Carol J Whitlatch ◽  
Sarah M Szabo ◽  
Evan G Shelton ◽  
Justin Johnson
Keyword(s):  
Service Providers ◽  
Early Stage ◽  
Care Planning ◽  
Early Stages ◽  
The Future ◽  
Rapport Building ◽  
Future Care ◽  
Future Data ◽  
Effective Decision Making ◽  
Planning Intervention

This article describes the implementation of SHARE (Support, Health, Activities, Resources, and Education), a counseling-based care-planning intervention for persons living with early-stage dementia and their family caregivers (CGs). The foundation of SHARE is built upon assessing and documenting the person living with dementia’s care values and preferences for future care. Using the SHARE approach, CGs are given an opportunity to achieve an understanding of their loved one’s desires before the onset of disease progression when the demand for making care decisions is high. Through working together with a SHARE Counselor, the care dyad begins to identify other sources of support, such as family and friends and service providers, in order to build a more balanced and realistic plan of care for the future. Data were collected from 40 early-stage dementia care dyads to determine the acceptability of having structured discussions about future care in the early stages of dementia. Findings from this study demonstrate the importance of planning in the early stages when persons with dementia can voice their care values and preferences for future care. Finally, this paper illustrates the use of supportive strategies such as rapport building, establishing buy-in, and communication to initiate care-related discussions with care dyads in the early stages that will help lead to more effective decision making in the future.

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