Data Analytics for Open Software Product Innovation

创新是在竞争激烈的市场中生存所必需的原则，软件产品也不例外。相反，软件现在是在一个开放的环境中开发的，在这种环境中，对知识、信息和资源的开放获取远远超出了传统的组织障碍，为创造创新的软件产品提供了新的机会。这些创新在软件功能、质量和及时交付方面尤为突出。本提案致力于促进开放式软件产品创新所需的新概念、理论、过程和方法。我所说的开放是指在获取信息方面的开放，在获取资源方面的开放，以及在用户参与创造新产品方面的开放。产品创新指的是(软件和基于软件的)具有“渐进式新”和“完全新”功能的产品。 这项提议的长期目标是进行跨学科研究，以发现促进软件产品创新的新方法。该提案的主要思想是，可以通过(I)将先前的经验综合为基于模式的建议以在正确的时间向正确的用户群提供正确的产品的想法来促进产品创新，(Ii)基于持续分析的使用和需求对功能和产品质量进行增量创新，(Iii)在包括用户、开发人员和客户的开放环境中对实时数据进行持续分析，以及(Iv)可管理地扩大人群智慧，以进行增量创新和持续评估。 我们正在研究促进开放式产品创新的四个发现领域： 产品创新的层次化方法，结合了基于模式的建议(大的)和优化(小的)。 对F和NF的分析-增量功能设计和开发，而不是对功能做出是或否的决定。 社交媒体和存储库分析，用于对功能需求、使用和有用性进行预测性和规范性建模。 面向众包开放产品开发的动态决策支持。 这项研究具有推测性，因为它以一种独特的方式结合和调整了不同研究领域的方法，使产品创新更加系统化和数据驱动。特别是，这包括来自其他领域和学科的方法和技术，如经济学、产品设计、优化和人工智能。 研究结果被植入一个名为Living Lab LILAPI的生活实验室学习和培训环境中。除了吸引学生并鼓励他们参与研究项目外，生活实验室的设计也是为了促进跨学科合作。作为LILAPI的关键应用之一，LILAPI可以为学习和培训使用移动应用程序在模拟紧急事件中快速决策提供一个半逼真的环境。

Innovation is the principle required for survival in competitive markets and software products are no exception. Rather, software is now developed in an open environment, where open access to knowledge, information and resources far beyond the traditional organizational barriers offers new opportunities to create innovative software products. These innovations are especially pronounced in terms of software functionality, quality and timely delivery. This proposal is devoted to new concepts, theories, processes and methods required to facilitate Open Software Product Innovation. By Open I mean openness in access to information, openness in access to resources, and openness in the involvement of users in creating new products. Product Innovation refers to (software and software-based) products with “incrementally new” and “completely new” features. The long term goal of this proposal is to perform interdisciplinary research to discover new ways to foster software product innovation. The main idea of the proposal is that product innovation can be facilitated by (i) the idea to synthesize former experience into pattern-based recommendations for delivering the right product at the right time to the right groups of users, (ii) incremental innovation on features and product quality based on continuously analyzed usage and demand, (iii) the continuous analysis of real-time data in an open environment including users, developers and customers, and (iv) manageable enlargement of crowd wisdom for incremental innovation and continuous evaluation. We are looking at four areas of discovery to facilitate Open Product Innovation: Hierarchical approach for product innovation combining pattern-based recommendations (in the big) and optimization (in the small). Analytics for F and NF-incremental feature design and development, instead of yes or no decisions towards features. Social media and repository analysis for predictive and prescriptive modeling of feature needs, usage and usefulness. Dynamic decision support for crowdsourced open product development. The research is speculative in the sense that it combines and adjusts methods from different areas of research in a unique way to make product innovation more systematic and data-driven. In particular, this includes methods and techniques coming from other areas and disciplines such as economics, product design, optimization and artificial intelligence. Results of the research are seeded into a living lab learning and training environment called living lab LILAPI. Besides attracting students and encouraging them to get involved in research projects, the living lab is also designed to foster interdisciplinary collaboration. As one of the key applications, LILAPI can provide a semi-realistic environment for learning and training of using mobile apps for rapid decision-making in simulated emergency events.