this in ludes the following ma ro-a tivities, with possible iterations, overlapping A rigorous and methodi al approa h distinguishes s ien e from other forms of and parallelization: understanding the validity of a hypothesis. Given the phenomenon to be studied, explanation be ause of its requirement of systemati experimentation and reprodu ibility. The s ienti method is re ognized as the basi resear h paradigm for and the formulation of a testable predi tion. Note that the pro ess often of a hypothesis. Evaluation, whi h in ludes the planning of an experiment able to assess tools, the gathered data, in order to gain insights, and possibly new knowlwhether the predi tion o urs or not. Observation: the a tivity of gathering fa ts and data about the phenomenon under study, often driven by the re ognition of an open problem or the draft Analysis: the a tivity of understanding, by means of manual or automati starts with a draft of su h a onje ture. edge apable to better explain the phenomenon. Formation of a hypothesis or a onje ture apable to explain the phenomenon, been proposed as solutions to support an innovation pro ess, even though they are mainly based on suggestions and 10-best-rules lists derived by personal tion pro ess, omparing the formulation of a hypothesis to the denition of an tual Enterprises. In Se tion 3 we re ognize some fun tional and non-fun tional This work is a ontribution to investigate the adoption of a pragmati and tion pro ess, with the aim to drive business innovation from the art towards of the latter; an open innovation perspe tive is introdu ed in the ontext of Viralways parti ularly ee tive, share the idea that business have to master the fun tionalities an be reused or adapted to provide more advan ed support for requirements for an ideal BI framework, together with some basi te hnologies experien e of business experts or innovation guru. Su h attempts, although not BI. Finally, Se tion 5 ontains some nal remarks. and tools that an be able to support it, while in Se tion 4 we make referen e to some spe i existing platforms supporting experimentations in e-S ien e, whose systemati approa h to support business users in the management of an innovai s ienti elds, espe ially in e-S ien e. In more details, Se tion 2 dis usses variables behind the innovation pro ess. the main similarities and dieren es between a s ienti pro ess and an innovainnovative idea, and proposing a s ienti approa h to estimate the ee tiveness the s ien e side, by also indi ating solutions that are already available for spe
enti dis iplines, nd in the s ienti method an approa h to onsistently systematize and formalize their method of inquiry, in order to develop more useful,
less stru tured than s ienti experimentations, and often hara terized by nonmassive volumes of data are produ ed by organizations daily: every produ t, esis). Su essive iterations allows, in both ases, to make use of the urrent and task, a tivity and pro ess that is planned or realized an be potentially tra ed fo us on the market, whi h is dynami and subje t to ontinuous and frequent It is to be noted that innovation pro esses, however, are in general mu h tion is a less mature dis ipline, whi h still la ks enough ba kground knowledge Nowadays, su h a perspe tive an be ee tively put in pra ti e [2℄. In fa t, and formalizations whi h onstitutes a ommon ba kground for s ientists. This and logged, and this onstitutes the pre onditions on whi h an observation an innovation pro esses, in luding the following a tivities: not known in advan e, and ould either ontradi t or validate the idea (hypothand theoreti al analysis to depi t a spe i methodology. hanges. For su h a reason, the re-exe ution of a BI pro ess, in general, may not (hypothesis). produ e identi al out omes. The s ienti method relies on a set of pra ti es in ludes, among others, methods and proto ols for evaluation and analysis, units be performed. Moreover, ee tive and mature te hniques are available to analyse provide a valuable improvement for innovation pro esses in the evaluation of an for planning and exe ution of a s ienti pro ess. Conversely, Business Innovaof measurements, standards and theories that represent a theoreti al framework enti approa h, based on methodi al management and analysis of data, ould The starting point of the investigation is represented by an idea, or a hypothea urate and omprehensive models and methods. In this broader sense, a s idata and to extra t knowledge from them. derlying some phenomenon in the physi al world, innovation pro esses has a A ording to su h a perspe tive, a general meta-pro ess ontrollable or non-observable variables that ould strongly ae t the nal result. Moreover, while s ienti investigation is aimed to dis over the stati rules unprevious out omes in order to ome out with a better explained or dened idea sis, whi h often arises from previous knowledge and the re ognition of an open problem. Both pro esses has a dynami and risky nature, be ause the output is innovation idea [1℄. what part of the internal pro ess should be modied. ness pro esses, logs about internal produ ts and tasks, together with related tors and ba kground knowledge ould be onsidered as well. nities. In fa t, the proper denition of an idea requires, at rst, to identify Observation, whi h is driven by prior knowledge or the draft of the idea, the ause-ee t relations between the open issues to solve and the stru tural Analysis of data in order to learly re ognize open problems and opportuand in ludes data trails about previous related innovation pro esses, busielements of the produ t/servi e or pro ess at hand. Su h a systemati apexternal data from the market, lients, and suppliers. Data about ompetiproa h ould help to point out whi h internal variables an be adjusted or an be devised for fun tional requirements of an ideal BI framework, together with the available tion, for ea h of the ma ro-a tivity of an innovation pro ess we identify the main A ording to the data-driven/open perspe tive introdu ed in the previous se te hnologies useful to provide the needed support: an be ooperatively olle ted and organized, are both a sour e and a driver for the innovation pro ess and a more open attitude towards ollaboration. In fa t, human de ision-making, and the ollaborative dimension (even within the same more important than in s ien e, be ause the pro ess is more strongly ae ted by lude the exploitation of new ollaborative business models and strategies like to be apable to reate a more dynami market.Open innovation pra ti es inreativity and human evaluation in the ontext of business innovation is even onsidered a ompetitive advantage.Su h a perspe tive, known as losed innoo-produ tion and o- reation, rowd-sour ing, peer produ tion, as well as the future pro esses in the VE. Despite its rigorous approa h, the s ienti pro ess is far from being an aubased on the usage of both internal and external resour es and ideas apable to imagination and reativity, espe ially for what on erns data understanding, hyversity: as a matter of fa t, the availability of pre- ompetitive knowledge proved pothesis generation and experiment planning. Similarly, an innovation pro ess ollaborative environments, like Virtual Enterprises, where information and ideas improvement of IT te hnologies for ommuni ation and information/knowledge Conventionally, the a hievement of innovation is based on the skills availinnovation. Then, the usage of proper tools and shared methodologies within the ontrol and ownership of intelle tual property. Re ently, also thanks to the requires a deep intera tion with business users. As a matter of fa t, the role of able within the boundaries of the ompany, and every improvement and idea is Companies ould greatly benet from exploiting both a s ienti approa h in usage of so ial te hnologies to support and oordinate ollaboration. vation, ultimately refers to ompanies as losed systems, and strongly relies on ompany) is mu h more prominent. reate opportunities for generating signi ant value. It is based on the notion the VE, together with a s ienti approa h to experimentation, an enhan e tomatable pre-dened pro edure to follow, be ause it strongly relies also on sharing, a new paradigm of open innovation is emerging [3℄. Open innovation is that knowledge annot be onstrained within a single ompany, team, and unithe su ess rate of innovation ideas and provide a basis useful as a referen e for terms of a set of indi ators and measures. lowed by an evaluation phase aimed at assessing the validity of the idea in Planning of an implementation and experimentation pro ess, whi h is folby the previous step. Formulation of an innovation idea, starting from the onsideration provided
about the domain and its open issues or aws. Data Mining and Knowlforman e Indi ators (KPI) should be used to gain insights about the pro ess fa ts at disposal about the produ t or pro ess under study. Useful te hnolosystems to analyse data in order to evaluate previously dened KPIs, apable Experimentation and evaluation, whi h require tools useful to support busiManagement systems (CRM), Workow Management Systems, Enterprise methods are ee tive solutions for su h a purpose. Systems for ollaborative edge Dis overy in Databases (KDD) algorithms, together with statisti al dis ussions and knowledge sharing, then, allow new ideas and suggestions to whi h steps ought to be taken in given ir umstan es, and whi h Key Perboth a summarized view of them and to extra t possible relevant hidden emerge. lude support tools to analyse the olle ted observations, in order to obtain CRM or surveys for information about ustomer satisfa tion and feedba k, Observation: the system should provide tools to support data gathering and storage from (possibly) multiple sour es, whi h allow to have eviden es and intermediate and output results, for instan e tra ing and logging systems, status. Moreover, during and after the exe ution, tools an be used to olle t relations, patterns or regularities, useful to gain new or learer knowledge ness users in planning the innovation pro ess, in luding suggestions about Resour e Planning, market analysis. gies in lude, besides databases and data warehouses, Customer Relationship to at h and show the impa t or the su ess of the innovation idea.
low to odify the dependen ies among the innovation pro ess’ a tivities, provide the planning and exe ution of an innovation pro ess. The framework should alapable to support a data-driven innovation pro ess: terprise’s, we envisage in the following the most hallenging problems that arise su h a basis we ontextually dene non-fun tional requirements for a framework Given that in this work we refer espe ially to environments like Virtual Enfrom the pe uliarities of su h an open, ollaborative and distributed s enario. On
Apart ommon ba kground knowledge like logi s or statisti s, spe i business of a shared orpus of standards, norms, rules, aimed at produ ing omparable, domains may require spe i solutions. For su h a reason, a framework should measurable and reliable results. Integration of knowledge from dierent s ienti elds is feasible thanks to the usage (and the sharing) of the same ommuni ation provide means to identify and des ribe resour es within the Virtual Enterprise partners. gies. Conversely, standards and pra ti es for BI have not been identied yet. by referring to the same terminology, to over ome the heterogeneities among the language, the same measurements units and ommon pra ti es and methodolowhi h omputation of massive datasets an be performed in a distributed manresour es, whi h are to be managed through spe i te hnologies, espe ially in ner. Also an innovation pro ess ould be potentially based on several distributed ially when innovation is highly data-driven or requires simulation. Distribution Given the data-intensive dimension of modern s ien e, espe ially the enterprise follows a more open approa h towards innovation, for instan e in tru tures like internet and the Web, spe i instruments are needed whenever in ertain elds, a re ent trend is the onstitution of virtual laboratories, in environments like Virtual Enterprise’s. Besides traditional ommuni ation infrassharing of knowledge/data, of distributed tools and even of omputation, espesuggestions about whi h spe i resour e to use in a given ontext, and in the mon IT te hnologies in lude data management systems, optimization algorithms hoi e of the best output indi ators. Complexity management also involves to and planning te hniques. keep tra k of the status of an innovation pro ess, its variables and outputs. Com
as modular and (possible) distributed servi es, is apable to improve exibilinto a ount previously identied requirements to sket h up the general approa h Servi e orientation, in whi h single tasks and a tivities may be onsidered provement of internal pro esses and pro edures, and the on epts around whi h ity and e ien y. In order to further maximize modularity and interoperability, appli ation of servi e approa h to enterprises [5℄. to reshape itself, and adopt a more exible attitude towards revision and imAspe ts of this proposal involve not only the adoption of ertain te hnololike in traditional SO ar hite tures, servi es an be des ribed by using the same of su h a system, aimed at supporting business innovation pro esses. the enterprise is organized. In this sense, one of the emerging solutions is the gies, but also several organizational hanges. This often requires the organization agerial, ollaboration an easily be ome a sour e of omplexity if not supported and to support a virtual team by putting together diverse ompeten ies and tion of a omplex pro ess typi ally require several skills, both te hni al and mana ontinuously growing interest in te hnologies for data, model and workow by any kind of oordination. In last years the s ienti ommunity is showing laborative way to s ien e. Similarly, Virtual Enterprises ould greatly benet of sharing (e.g., [4℄), whi h onstitute the ba kbone of a more networked and olsystems to share information, data and ideas among the distributed partners, apabilities, and providing means to manage oordination and to o-operatively perform tasks and a tivities. Collaboration and oordination Sin e the ooperative planning and exe uto address integration problems. Advan ed fun tionalities an rely both on servi e and pro ess repositories by using semanti te hnologies to dene a ommon terminology (at least, shared (1) to understand whi h servi es are usually applied after a given one, or (2) to provide suggestions about whi h (typology of) servi e is re ommended in a ertain stage of the innovation pro ess, and (3) to dis overy the most ommon among the members of an organization or among the partners of a VE) allows pra ti es of usage of ertain servi es. Moreover, the des ription of su h servi es thing. Portfolio Hard over (2006) 2. Kusiak, A., Tang, C.Y.: Data-inspired innovation model. In: Pro . of the 36th 5. Poulin, M.: Ladder to SOE: How to Create Resour eful and E ient Solutions for erna: a tool for building and running workows of servi es. Nu lei A ids Resear h 16th International Conferen e on S ienti and Statisti al Database Management, 4. Roure, D.D., Goble, C., Stevens, R.: The design and realisation of the myexperiment Dis overy. In: Pro . of the IEEE International Symposium on Collaborative Te hfor innovation. Harvard Business S hool Press Books (2003) 9. Diamantini, C., Potena, D., Storti, E.: A Semanti -Aided Designer for Knowledge 6. Hull, D., Wolsten roft, K., Stevens, R., Goble, C., Po o k, M., Li, P., Oinn, T.: TavComputer Systems 25(5) (2009) 561 567 an extensible system for design and exe ution of s ienti workows. In: Pro . of 1. Thomke, S.: Experimentation matters: Unlo king the potential of new te hnologies 2006) 18 International Computers and Industrial Engineering Conferen e, C&IE 2006. (June 3. Taps ott, D., Williams, A.: Wikinomi s: How Mass Collaboration Changes Every7. Altintas, I., Berkley, C., Jaeger, E., Jones, M., Ludas her, B., Mo k, S.: Kepler: on The Semanti Web: Resear h and Appli ations. ESWC ’07, Berlin, Heidelberg, Market Changes within Business and Te hnology. Troubador Publishing Ltd (2009) virtual resear h environment for so ial sharing of workows. Future Generation 8. Bernstein, A., Dnzer, M.: The next system: Towards true dynami adaptations of Springer-Verlag (2007) 739748 nologies and Systems, Philadelphia, PA, USA, IEEE (May 23-27 2011) 8674 34 (2006) 729732 Santorini, Gree e (June 2004) 423424 semanti web servi e ompositions. In: Pro eedings of the 4th European onferen e
Referen es a BI framework ould signi antly help in better dis riminating the best from s ienti and methodi al approa h an be adopted in the ontext of innovation the theoreti al analysis and to propose an ar hite ture of su h a BI framework. trol over the pro ess and redu ing the risks of failure. Currently, the la k of tion of su h prin iples. To this aim, we proposed a set requirements and te hThe dis ussion provided in this work is aimed at analysing at what extent a organization, this is espe ially true in ollaborative environments, where shared methodologies ould be identied starting from the information/pro esses that both to provide support to BI pro esses design and management, and a means to the worst pra ti es in business innovation pro esses, whi h an be onsidered as devise and test theoreti al and pra ti al models and methods for BI. Ultimately, methodologies for BI represents the major hindran e against the a tual appli abe useful to make innovation pro esses more e ient/ee tive within a single the rst step towards the denition of methodologies for BI. Although it ould are shared among the partners. As future work we would like both to deepen pro esses to estimate the ee tiveness of an innovative idea, in reasing the onnologi al solutions that an onstitute the basis of a future framework, aimed