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News
October 12, 2026
Road Injuries Leading Cause of Death Among Men Aged 10 to 39
Worldwide, road injuries are the leading cause of death among males aged 10–39. An international team of researchers reached this conclusion as part of the Global Burden of Disease Study 2023, analysing data from 204 countries between 1990 and 2023. Their findings have been published in The Lancet Public Health, with HSE University Professor Vasily Vlassov listed among the collaborators.
October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.

 

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Anticipating Future Innovation Pathways Through Large Data Analysis

Netherlands : Springer, 2016.
Under the general editorship: T. Daim, D. Chiavetta, A. Porter, Saritas O.

This book aims to identify promising future developmental opportunities and applications for Tech Mining. Specifically, the enclosed contributions will pursue three converging themes:

  • The increasing availability of electronic text data resources relating to Science, Technology & Innovation (ST&I)
  • The multiple methods that are able to treat this data effectively and incorporate means to tap into human expertise and interests
  • Translating those analyses to provide useful intelligence on likely future developments of particular emerging S&T targets.

Tech Mining can be defined as text analyses of ST&I information resources to generate Competitive Technical Intelligence (CTI). It combines bibliometrics and advanced text analytic, drawing on specialized knowledge pertaining to ST&I. Tech Mining may also be viewed as a special form of “Big Data” analytics because it searches on a target emerging technology (or key organization) of interest in global databases. One then downloads, typically, thousands of field-structured text records (usually abstracts), and analyses those for useful CTI.  Forecasting Innovation Pathways (FIP) is a methodology drawing on Tech Mining plus additional steps to elicit stakeholder and expert knowledge to link recent ST&I activity to likely future development.  A decade ago, we demeaned Management of Technology (MOT) as somewhat selfsatisfied and ignorant.  Most technology managers relied overwhelmingly on casual human judgment, largely oblivious of the potential of empirical analyses to inform R&D management and science policy.  CTI, Tech Mining, and FIP are changing that.

Chapters
Tech Mining for Emerging STI Trends Through Dynamic Term Clustering and Semantic Analysis: The Case of Photonics
Bakhtin P., Saritas O., , in: Anticipating Future Innovation Pathways Through Large Data Analysis.: Netherlands: Springer, 2016. P. 341–360.
Technology mining (TM) helps to acquire intelligence about the evolution of research and development (R&D), technologies, products, and markets for various STI areas and what is likely to emerge in the future by identifying trends. The present chapter introduces a methodology for the identification of trends through a combination of “thematic clustering” based on the ...
Added: June 20, 2016
Recent Trends in Technology Mining Approaches: Quantitative Analysis of GTM Conference Proceedings
Mikova N., , in: Anticipating Future Innovation Pathways Through Large Data Analysis.: Netherlands: Springer, 2016. P. 59–69.
This paper performs a quantitative analysis of trends in technology mining (TM) approaches using 5 years (2011–2015) of Global TechMining (GTM) conference proceedings as a data source. These proceedings are processed with a help of Vantage Point software, providing an approach “tech mining for analyzing tech mining.” Through quantitative data processing (bibliometric analysis, natural language processing, ...
Added: June 20, 2016
Research target: Computer Science Economics and Management
Priority areas: management IT and mathematics business informatics
Language: English
Full text
DOI
Text on another site
Keywords: industrial organizationbig datainnovation/technology managementtechnology policybig data analyticsR&D policy
Anticipating Future Innovation Pathways Through Large Data Analysis
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