Innovative design methods for data science - beyond brainstorming
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This document discusses potential contributions of design theory and methods for data science projects. It begins by introducing design theory, particularly C-K design theory which describes innovative design as the interaction and joint expansion of concepts and knowledge. It then presents two design methods - Innovation Field Mapping which uses C-K theory to map out an innovation field, and the KCP process which involves collective reasoning and action on concepts and knowledge. The document argues that these design theory approaches can help data science initiatives generate high-potential projects that can lead to breakthrough results, beyond traditional brainstorming.
![DKCP process: Linearising C-K dynamics
20!
Akın O. Kazakçı, MINES ParisTech!
Proven
methodology:
-‐
Developped
at
Mines
ParisTech
(TMCI)
with
RATP
and
Thalès
Avionics
-‐ 40+
KCP
by
researchers
(2002-‐2014)
-‐ 2
PhD
Projects
(Arnoux,
2013;
Klasing
Chen,
in
process)
-‐ Now,
a
network
of
specialist
consultants
Ini4alisa4on
[K]
Knowledge
sharing
Workshops
[P]
Project
building
[C]
IFM-‐Design
Workshops
[RUN]](https://image.slidesharecdn.com/cdscreativitydatasciencekazakciminesparistech-150108054913-conversion-gate01/85/Innovative-design-methods-for-data-science-beyond-brainstorming-20-320.jpg)
![DKCP process: Linearising C-K dynamics
28!
Management
of
the
cogni4ve
and
social
aspects
(KCP
facilitators)
Innova4on
effort
(Par:cipants;
20-‐50)
D
K
C
P
Pré-‐C
Pré-‐K
Project
organisa:on
Defining
and
pre-‐explora:on
of
K
pockets
Sharing
and
integra:ng
K
Orienta:on
of
phase
C
Guided
crea:vity
Building
ac:onnable
strategies
Akın O. Kazakçı, MINES ParisTech!
Ini4alisa4on
[K]
Knowledge
sharing
Workshops
[P]
Project
building
[C]
IFM-‐Design
Workshops
[RUN]](https://image.slidesharecdn.com/cdscreativitydatasciencekazakciminesparistech-150108054913-conversion-gate01/85/Innovative-design-methods-for-data-science-beyond-brainstorming-28-320.jpg)
Innovative design methods for data science - beyond brainstorming
- 1. Innovative design methods for data science projects - beyond brainstorming Akın O. Kazakçı akin.kazakci@mines-paristech.fr Centre for Data Science January the 7th, 2014
- 2. Plan 1. Introduction! 2. Potential contribution of design theory! 2! Akın O. Kazakçı, MINES ParisTech!
- 3. Design Theory and Methods for Innova4on • Chair for Research and Educa:on • Fundamental Research on Design Theory • 11 Industrial Sponsors • Theory , Field research, History, Laboratory experiments
- 4. CDS; Peculiar Characteristics & Lots of Unknown • What is data-science? – You have 10 secs. Please avoid dictionary definitions. And no, do not use a list of subdomains. • Is this a new form of organisation? Which model? – Neither private R&D, nor traditional research lab. • How to unify and align researchers interests? – Would traditional incentives be enough? • What is the overall project for CDS? – How to build a joint long-term vision with clearly articulated (scientific or not) objectives? 4! Akın O. Kazakçı, MINES ParisTech!
- 5. Gartner’s Hype Cycle 5! Akın O. Kazakçı, MINES ParisTech! Cabane et al. 2014, Understanding the Role of Collective Imaginary in the Dynamics of Expectations Int. Prod. Dev. Mana. (IPDM) Conf.
- 6. Are there strategies that would allow « smooth landing »? 6! Akın O. Kazakçı, MINES ParisTech! Average DSI Curve « Smooth-Lander » DSI Innovative DSI How to reach plateau of produc:vity? How to reach it before others and lead the way? Which methods, processes or principles would allow building innova:on strategies for DSIs? How would a data science ini:a:ve (e.g. centres or groups) generate high-‐poten:al projects that can lead to breakthrough results?
- 7. Plan 1. Introduction! 2. Potential contribution of design theory! 7! Akın O. Kazakçı, MINES ParisTech!
- 8. Profound Transformation of NPD activities 8! Akın O. Kazakçı, MINES ParisTech! • New functional spaces • New user experiences • New competencies • New partnerships • New business models • Fuzzy industrial sectors è 3rd Industrial revolution (Le Masson et al., 2006) è New Products vs. New Product Types è Revision of Objects’ Identities (Hatchuel et al., 1999)
- 9. New products vs. New product categories ? ? ? ? ? A300 A340
- 10. Main functions and design parameters are maintained Rule-‐based design Rule-‐breaking design • New functional spaces • New competencies • New partnerships • New business models Innova4on: op4misa4on or iden4ty change? Innova:on as « op:misa:on » Innova:on as « iden:ty change »
- 11. 11! Akın O. Kazakçı, MINES ParisTech! How to capture revision of identities? – A concept-‐knowledge theory of design « Design specs » Tradi:onal Object Defini:ons: Knowledge Methods, Judgements, R&D Competencies… an example of design specs for locomo:ve engines (1890s’) In design, objects can be defined by a « design spec » -‐ a list of features (or proper:es). The designer (individual or group) need to have some knowledge specific to each « feature » to be able to implement (or build) it and for handling interac:ons.
- 12. Revision of identities as « Dual expansive reasoning » ? ? Concept expansions Knowledge expansions In « innova:ve design », both design specs and associated knowledges are « dissolved » and « made to evolve ».
- 13. Source: Wikipedia Hatchuel 96; Hatchuel and Weil 99, 02 Kazakci and Tsoukias, 03; Kazakci 07 13! C-K design theory: a breakthrough in understanding design C-‐K design theory describes innova:ve design as the interac:on and joint expansion of concepts and knowledge. Ø Collec:ve reasoning and ac:on on desired, unknown and undecidable objects Ø Two spaces for exploring: Space of concepts (arborescent explora:on of unfeasible specifica:ons) and knowledge space (proposi4ons about the world – all kinds of knowledge). Ø Opera4ons for iden4ty change : Expansive par44ons (flying ship, free newspaper, mobile museum, camera-‐ glass, … ) A revival of design theory field: Yoshikawa, 81; Suh, 91; Braha and Reich 03; Shai and Reich, 03; Research in Engineering Design, Special Issue on Design Theory (2013), …
- 14. Plan 1. Introduction! 2. Potential contribution of design theory! Methods:! – Innovation Field Mapping! – KCP Process! 14! Akın O. Kazakçı, MINES ParisTech!
- 15. 15! Akın O. Kazakçı, MINES ParisTech! Brainstorming is not enough !!!
- 16. Concept Knowledge Classic K New K for motorist 16! Akın O. Kazakçı, MINES ParisTech! C-K for Innovation Field Mapping What is the Open Rotor innova4on field ? Project with Snecma Brogard, Joanny, 2010 Chaire TMCI
- 17. Exploring the classic engines improvements Changing plane and flying experience - How to go beyond tradi4onal design paths? 17! Akın O. Kazakçı, MINES ParisTech! C-K for Innovation Field Mapping
- 18. monitoring progress with CrossValida:on + Achieve 5σ! Select a classifica:on method! Pre-‐processing! Choose hyper-‐params! Train! Op:mize for accuracy! SVM Decision Trees NN …..….. Integrate AMS directly in training during Gradient Boos:ng (John) during node split in random forest (John) Weighted Classifica:on Cascades Two par:cipants observe that AMS can be refactorized and its terms can be rewrimen in terms of their convex conjugate form – which allow to Fenchel-‐Young inequality from convex op:miza:on limerature. Ref: hmp://arxiv.org/pdf/1409.2655v2.pdf, Mackey & Brian Op:miza:on of AMS becomes possible by a procedure they name Weigthed Classifica/on Cascades.(Rank: 461th) ? ? ? ? ? Gradient boos:ng methods fit a classifier to the 'per data point loss' and since AMS is not a sum of per data point (event) losses, it's not obvious how to do use AMS as a loss in gradient boos:ng (Andre Holzner) AMS: 3.3 è The node split works by looking for the split that maximises the AMS of one side of the split when predic:ng it as pure signal (John) An alterna:ve may be to « use AUC in gradient boos:ng :ll you get to the max cv result and then tried to move forward with an AMS loss func:on from that point » In principle, the AMS approximate func4on is derivable (hmp://:nyurl.com/ov5pedq) at a node level (s and b being the totals of other nodes, considered constant, and x, w being the probability predic:on and weight for the node to be split) and one could rewrite the part of code where the objec:ve func:on is evaluated, replacing the sums with a different calcula:on » (Giulio Casa) C space K Space Design for sta:s:cal efficiency 1st 2nd 3rd ensembles + selec:ng a cutoff threshold that op:mise (or stabilise AMS) Design strategy analysis for HiggsML challenge teams
- 19. Reduce within-‐class imbalance C K Dealing with CIP By adjus4ng class distribu4on Working in input space Re-‐represen4ng inputs Local distor4on Produce an embedding Change spa4al resolu4on For some X X is a support vector With raw data Feature engineering Exploratory (knowledge or intui4on based Automated Gene4c Algoritms (Wasilowski, Chen, 2009) Reduce between-‐class imbalance Reduce both Costs are known Oversampling signals Undersampling the background Iden4fying class distribu4on Progressive sampling by duplica4ng by synthesizing new points SMOTE, (Chawla, Bowyer et al. 2002) MSMOTE (Hu et al, 2009 ) Borderline SMOTE (Han et al, 2005) ) Adap4ve Synthe4c Sampling (He et al, 2008 ) SafeLevel Sampling (Bunkhumpornpat et al 2008 ) resample each mixture contains all signals + some background Such that all background points are used at least in one mixture Use meta-‐learning (Chan, Stolfo, 2001) Use SVM ensemble (Yan, Lin et al, 2003) Remove reduntant (Kubat, Matwia, 1997 Remove border regions with background examples (Kubat, Matwia, 1997) Reduce overlap Preferen4al sampling Remove background whose average distance to its 3 NN is smallest (Mani, Zhang, 2003) By adap4ng algorithms Improve predic4ve accuracy Reduce predic4ve variance Alterna4ve search techniques Non-‐greedy methods Gene4c Alg. Detect rare events TimeWeaver ( ) Discover small disjuncts (Carvahlo, Freitas, ) Change evalau4on metrics Simulated Annealing Depth-‐bound exhaus4ve Brute () Laplace es4mate Evaluate small disjuncts separately Quinlan, () Modify defini4on of learning Bias induc4on towards specificity Minimize error costs Change levels of learning Cascade of learners Learn only rare class () Two-‐level learnig () Unknown Costs Modify base learner Max Specificity (Acker, Porter, 1989) Specificity for small disjuncts (Ting, 1989) Base is a Tree Learner Split aoributes are selected to minimise total expected cost Base is a NN Cost-‐weighted error propaga4on Relabeling for min expected cost Test data Training data Weigh4ng (Ting, 1998) CSC (Wioen, Franck, 2005) MetaCost (Domingos, 1999) Cos4ng (Zadrony et al, 2003) Preprocess ing Cost-‐based sampling Empirical Threshold Sepng Plot total cost for various thresholds Choose min using plot With Cross Valida4on by choosing less steep hills Thresholding (Sheng, Ling, 2006) Using ensembles Using cross valida4on Cost-‐ Sensi4ve Boos4ng Imbalance d IVotes () AdaCost ( ) Using sampling to alter weight distribu4on Boos4ng CSB () RareBoost ( ) MSMOTE Boost () SMOTE Boost () Data Boost-‐ IM () RUSBoost () Bagging Overbagging ( ) Underbagging () Under-‐ Over-‐ Bagging () Dicovery Problem Cross-‐Va Ensemb Gradient loss' and losses, it boos:ng AMS: 3.3 maximise as pure s An altern you get t with an A In princip (hmp://: the total being th be split) objec:ve different 1 2 3 4 5 Data science as a new fron:er for design A. Kazakci, ICED’15 (submimed)
- 20. DKCP process: Linearising C-K dynamics 20! Akın O. Kazakçı, MINES ParisTech! Proven methodology: -‐ Developped at Mines ParisTech (TMCI) with RATP and Thalès Avionics -‐ 40+ KCP by researchers (2002-‐2014) -‐ 2 PhD Projects (Arnoux, 2013; Klasing Chen, in process) -‐ Now, a network of specialist consultants Ini4alisa4on [K] Knowledge sharing Workshops [P] Project building [C] IFM-‐Design Workshops [RUN]
- 21. Try it! -‐ Red Bull Gravity Challenge You are a designer and you have been asked to produce the most crea:ve solu:on to the following ques:on: Ensure that a hen's egg dropped from a height of 10m does not break.” Agogué©. Being innova:ve: how easy is that? Your turn!
- 22. Experiments with 210 subjets (842 proposi/ons) “Fixa4on effects” Three types of solu:ons : Slowing the fall Protec:ng the egg Dumping the schock covers 81 % results! Fixa:ons on an objects iden:ty You got anything beKer ???
- 23. Determining expansive path using C-K reasoningDetermining fixation path using C-K reasoning Theory-driven experiments – SIG Design Theory 2012 – M.Cassotti & M.Agogué C space K space Expanding both in the C-space and in the K-space for the “egg” task
- 24. Result 1 : the paths identified as fixation paths using C-K theory are the ones within the fixation effect for adults Theory-driven experiments – SIG Design Theory 2012 – M.Cassotti & M.Agogué (1) Natural distribution of solutions of a design task
- 25. Types of « fixation » based on C-K theory 25! Akın O. Kazakçı, MINES ParisTech! Cogni:ve fixa:ons Social fixa:ons
- 26. Limits of traditional methods for collective creativity Consensus& Shared understanding Originality Participative Seminars Creative Commandos è Classical methods do not allow generating concepts that are both breakthrough and shared! Fixa:on Phenomena Isola:on Phenomena 26! Akın O. Kazakçı, MINES ParisTech!
- 27. DKCP : Organising for shared breakthrough projects Consensus& Shared understanding Originality Fixa:on Phenomena Isola:on Phenomena A method for steering breakthrough process 27! Akın O. Kazakçı, MINES ParisTech!
- 28. DKCP process: Linearising C-K dynamics 28! Management of the cogni4ve and social aspects (KCP facilitators) Innova4on effort (Par:cipants; 20-‐50) D K C P Pré-‐C Pré-‐K Project organisa:on Defining and pre-‐explora:on of K pockets Sharing and integra:ng K Orienta:on of phase C Guided crea:vity Building ac:onnable strategies Akın O. Kazakçı, MINES ParisTech! Ini4alisa4on [K] Knowledge sharing Workshops [P] Project building [C] IFM-‐Design Workshops [RUN]
- 29. Thank you! Disclaimer: Copyrights of images belong to their respective owners. 29! Akın O. Kazakçı, MINES ParisTech! Akın O. Kazakçı akin.kazakci@mines-paristech.fr Feel free to contact me for more: