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008			160725s2016 gw | s |||| 0|eng d
020			_a9783319390567
040			_aES-MaUEC
050		4	_aQA76.9.D343 _bA585 2016 EB
082	0	4	_a658.514
245	1	0	_aAnticipating Future Innovation Pathways Through Large Data Analysis _cedited by Tugrul U Daim, Denise Chiavetta, Alan L Porter, Ozcan Saritas
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XVIII, 360 p.) _b141 ilustraciones, 108 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	1		_aInnovation, Technology, and Knowledge Management _x2197-5698
505	0		_aPreface -- Part I: Data Science/Technology Review -- Chapter 1: FTA as Due Diligence for an Era of Accelerated Interdiction by an Algorithm-Big Data Duo -- Chapter 2: A Conceptual Framework of Tech Mining Engineering to Enhance the Planning of Future Innovation Pathways -- Chapter 3: Profile and Trends of FTA and Foresight -- Chapter 4: Recent Trends in Technology Mining Approaches -- Chapter 5: Anticipating Future Pathways of Science, Technology, and Innovations -- Part II: Text Analytic Methods -- Chapter 6: Towards Foresight 3.0--The HCSS Metafore Approach -- Chapter 7: Using Enhanced Patent Data for Future-Oriented Technology Analysis -- Chapter 8: Innovation and Design Process Ontology -- Chapter 9: Generating Competitive Technical Intelligence Using Topical Analysis, Patent Citation Analysis and Term Clumping Analysis -- Chapter 10: Identifying Targets for Technology Mergers and Acquisitions Using Patent Information and Semantic Analysis -- Chapter 11: Identifying Technological Topic Changes in Patent Claims Using Topic Modeling -- Chapter 12: Semi-Automatic Technology Roadmapping Composing Method for Multiple Science, Technology, and Innovation Data Incorporation -- Chapter 13: Generating Futures from Text -- Part III: Anticipating the Future--Cases and Frameworks -- Chapter 14: Additive Manufacturing -- Chapter 15: The Application of Social Network Analysis -- Chapter 16: Building a View of the Future of Antibiotics Through the Analysis of Primary Patents -- Chapter 17: Combining Scientometics with Patent-Metrics for CTI Service in R&D Decision Making -- Chapter 18: Tech Mining for Emerging STI Trends through Dynamic Term Clustering and Semantic Analysis: The Case of Photonics. .
520	3		_aThis 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 zBig Datay 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 self-satisfied 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. The accumulation of Tech Mining research over the past decade offers a rich resource of means to get at emerging technology developments and organizational networks to date. Efforts to bridge from those recent histories of development to project likely FIP, however, prove considerably harder. One focus of this volume is to extend the repertoire of information resources; that will enrich FIP. Featuring cases of novel approaches and applications of Tech Mining and FIP, this volume will present frontier advances in ST&I text analytics that will be of interest to students, researchers, practitioners, scholars and policy makers in the fields of R&D planning, technology management, science policy and innovation strategy
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, EBSPRINGER
650		7	_aData mining _0comprobar BNE20033218554 _2embne _9162648
650		7	_aInnovaciones tecnológicas _0comprobar BNE20011324662 _2embne _9405746
700	1		_aDaim, Tugrul Unsal, _d1967- _eeditor literario _0Local _996904
700	1		_aChiavetta, Denise _eeditor literario _999555 _0Local
700	1		_aPorter, Alan L. _eeditor literario _999556 _0Local
700	1		_aSaritas, Ozcan _eeditor literario _999557 _0Local
830		0	_aInnovation, Technology, and Knowledge Management _x2197-5698 _9134072
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-39056-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319390567
907			_a.b12953891 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b11-07-17 _cm _dz _ek _feng _ggw _h0
945			_aQA76.9.D343 A585 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i1159634x _z06-04-17
999			_c86111 _d86111 _x1