“DAIS-ITA”的意思、由来-开放百科全书

The International Technology Alliance in Distributed Analytics and Information Sciences (DAIS-ITA) is a research program initiated by the UK Ministry of Defence (United Kingdom) (MoD) and the US Army Research Laboratory (ARL), in September 2016.^[1] It is led by IBM Research in the U.S. and IBM Hursley in the UK. DAIS ITA is the second International Technology Alliance started by the two countries, succeeding the previous ten year alliance NIS-ITA, which was of similar nature.

Overview

The complete name of the alliance is United States/United Kingdom International Technology Alliance in Distributed Analytics and Information Sciences. It is a research group tasked with conducting fundamental research in distributed analytics and information science which affects coalition operations.

DAIS ITA is the second such program supported by collaborative arrangement between U.S. and UK governments. The previous program NIS-ITA ran from 2006-2016, and was also led by IBM. However, the technical scope as well as the composition of the two alliances are very different. The focus of DAIS-ITA is on distributed analytics, while the focus of NIS-ITA was on different types of network, or network science. The alliance consists of researchers from ARL and MoD working together with several UK and US based industries and universities to solve fundamental research problems related to distributed analytics.

The DAIS ITA research focuses on two technical areas, (i) Dynamic Secure Coalition Information Infrastructures and (ii) Coalition Distributed Analytics and Situational Understanding.

History

Since approximately 1992, ARL formed a number of partnerships that involved the triad of industry, academia and government. One of them was the Distributed Analytics and Information Science (DAIS) International Technology Alliance (ITA) which was awarded September 21, 2016.^[2] The program was to be completed in September 2025.^[3]

Objective

The stated objective of DAIS was “to enable secure, dynamic, semantically-aware, distributed analytics for deriving situational understanding in coalition operations.” With US/UK coalition operations becoming increasingly more complex, DAIS sought to address burdens placed on people and technologies that were deployed.^[4]

Research Thrusts

The DAIS program was organized around several research thrusts, including the following:^[5]

Members

DAIS-ITA consists of several research laboratories and universities in U.S. and UK. These include the U.S. Army Research Laboratories and the UK Defense Science and Technologies as participating Government research laboratories. The non-government members of the alliance in both countries consisted of both industrial and academic research organizations as listed below:

U.S. Universities

UK Universities

U.S. Industry

UK Industry

Leadership

The alliance is being led by a team consisting of technical leaders from the U.S. Army Research Laboratory, UK MoD (Dstl), IBM Research in the U.S. and IBM UK.

Since the inception of the program, the following people have served in the role of alliance leader listed above:

Both of the technical areas is led by a panel of four scientists, one each from the U.S. Army Research Laboratory, UK MoD (Dstl), an academic member of the alliance, and an industrial member of the alliance. The following is a table of researchers who are currently acting in the role of the technical area leaders (TAL) for each of the technical areas within the scope of the research program.

Results

Examples of research results developed by the DAIS program as of 2018 include the following.

References

1. ^{{cite news|last1=Clemens|first1=Jay|title=Army Research Lab, UK MoD Form Distributed Analytics & Info Science Alliance|url=http://www.executivegov.com/2016/09/army-research-lab-uk-mod-form-distributed-analytics-info-science-alliance/|agency=Army Research Labs|date=26 September 2016}}
2. ^{{Cite web|url=https://www.arl.army.mil/www/default.cfm?page=93|title=Collaborative Alliances {{!}} U.S. Army Research Laboratory|website=www.arl.army.mil|language=en|access-date=2018-09-05}}
3. ^{{Cite web|url=http://www.signal-digital.com/signal/february_2017?pg=35#pg35|title=Signal - February 2017 - page35|website=www.signal-digital.com|language=en|access-date=2018-09-05}}
4. ^{{Cite web|url=https://www.arl.army.mil/www/default.cfm?page=2499|title=Distributed Analytics and Information Science (DAIS) ITA {{!}} U.S. Army Research Laboratory|website=www.arl.army.mil|language=en|access-date=2018-09-05}}
5. ^{{Cite web|url=https://www.arl.army.mil/www/default.cfm?page=3049|title=Distributed and Collaborative Intelligent Systems and Technology (DCIST) {{!}} U.S. Army Research Laboratory|website=www.arl.army.mil|language=en|access-date=2018-09-05}}
6. ^{{cite arxiv|last=Zafari|first=Faheem|last2=Li|first2=Jian|last3=Leung|first3=Kin K.|last4=Towsley|first4=Don|last5=Swami|first5=Ananthram|date=2017-12-10|title=Optimal Energy Tradeoff among Communication, Computation and Caching with QoI-Guarantee|eprint=1712.03565|class=cs.NI}}
7. ^{{Cite web|url=http://www.commsp.ee.ic.ac.uk/~faheem/arxiv2017sig.pdf|title=What, When and Where to Cache: A Unified Optimization Approach|last=|first=|date=|website=|archive-url=|archive-date=|dead-url=|access-date=}}
8. ^{{Cite web|url=https://researcher.watson.ibm.com/researcher/files/us-wangshiq/SW_INFOCOM2018.pdf|title=When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning|last=|first=|date=|website=|archive-url=|archive-date=|dead-url=|access-date=}}