Introduction for the special Issue on BIG DATA

American recently declassified records give evidence that Qaddafi did not meet any signifiToday we live in the era of Big Data Revolution, overwhelmed with data, information and knowledge that is spread all over the web, in social media sites, in smartphones contributing so in creating a large base of big data. In their best seller book ‘Big Data: A Revolution That Will Transform How We Live, Work, and Think’ (2013) Mayer-Schönberger and Cukier argue that thanks to the internet, social networking, smartphones and credit cards, more data is being collected and stored about us than ever before – and this has created an opportunity for firms and managers to easily and cheaply capture and store massive amounts of data in a way that was simply impossible before. In this scenario the winners will be those that have the abilities, the intelligence, the creativity and the tools to elaborate these data for grasping insights and knowledge from the available data and to be able to use and exploit them for continuous innovation, for improving firms performance, for creating new products and services as well as for establishing new innovative business models. According to a study realized by McKinsey (Manyika et al, 2011) to analyze the impact of big data analysis for innovation, competition, and productivity they argue that there are 5 broad ways in which using data can create value: 1.Big data can unlock significant value by making information transparent and usable at much higher frequency. 2.As organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance. 3.Big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services. 4.Sophisticated analytics can substantially improve decision-making. 5.Big data can be used to improve the development of the next generation of products and services. On the other hand, the availability of big data has created a new era also for data analysis and elaboration to discover unknown patterns, to find out what customers want, what they evaluate, to get closer to customers, to gain a wealth of information about their behaviors and preferences, as well as to identify new market trends and new opportunities to remain competitive. The ability of firms to aggregate, elaborate and analyze the data is becoming a key competitive advantage resource. Different researches have evidenced the role and the outcomes that the big data analysis could bring to firms in terms of innovation, efficiency, productivity, quality and customer satisfaction. In a survey study of more than 3,000 business executives, managers and analysts from organizations, MIT Sloan Management Review, in collaboration with the IBM Institute for Business Value found out that executives are oriented toward managing the business based on data-driven decisions and it is the use of business information and analytics that differentiates them within their industry (Lavalle et al, 2010). Moreover, the content and information that customers create in web 2.0 platforms constitute a valuable asset for firms to directly tap into the customer’s preferences and needs, as the most valuable source for attaining direct and reliable market information. In this environment, more and more firms are building their competitive advantage on their ability to collect, analyze and act on data. Therefore, the capability of firms to tap into data, to analyze and interpret them to gain insights and to ensure a more effective decision making process has become an essential ingredient towards innovative thinking and creativity. Therefore, there is a need of matching the analytical capacities with the creativity in order to interpret big data in an innovative way. It is in this aim that we have realized these special issue of the journal to publish some research articles that use statistical and analytical models to elaborate big data for a large range of issues and sectors and for establishing new innovative insights. The issue presents four research papers that focus on providing practical cases of exploiting big data for grasping new insights and opportunities. In particular: The article on ‘Big Data and Knowledge-intensive entrepreneurship: trends and opportunities in the tourism’, the authors Del Vecchio et al., focuses on the growing relevance of Big Data as valuable source of knowledge impacting on the creation and execution of knowledge-intensive entrepreneurship. The article provides a detailed description regarding the opportunities offered by Big Data by demonstrating, with practical applications from the tourism field, how the large amount of knowledge distributed in the web can support the conception and execution of an entrepreneurial process more aligned with the customers' needs and focused on the actual market's trends. Carpita and Simonetti in the article ‘Big Data to Monitor Big Social Events: Analysing the mobile phone signals in the Brescia Smart City’ present the implications that big data analysis could provide for Municipal administration to plan future events, and more generally to develop policies for the ‘smart city. They use the statistical methods to process and assess really high mass of data and information extracted from mobile phone signals in order to improve the quality of the big social events that take place in the city as well as to create the conditions for developing useful reports for territorial marketing. The paper on ‘Ontological analysis for dynamic data model exploration’ by Hobbs et al., explores the expressive approaches to data analysis. The authors provide an aggregate model that utilizes ontological tools to create domain models in a way that it allows for a distributed and parallel implementation necessary for big data analysis. In the article ‘Implementation of a Web-based Application for Predicting Best Training Recommenders for Princess Norah University Employees’ the authors Mohammad and Alhaidey propose the realization of a recommender system that would help in the decision making process and planning of the training course offered by organizations for their employees. The recommender system is based on using data mining techniques that allows the observer to discover specific patterns and knowledge from large databases and carrying out predictions for outputs.cant opposition from the Nixon Administration during his first years in power. Washington had realised that producers had acquired control of their natural resources, and only pursued continued access to Middle Eastern energy sources. Moreover, all Western capitals hoped that the young dictator would turn into a stronghold against Russian influence in the area at issue. After all, US companies had not been expelled from Libya, whose market was growing more and more towards Western Europe. In a few words, each time policy makers suggested a course of action rather hostile to Tripoli, this option was always excluded as non profitable to American interests. All this shows that the Nixon Administration was trying to “contain” the Libyan regime, at the same time building a dam against the Soviets, who were the real target of Nixon’s Twin Pillars Policy.

美国近期解密的档案显示，卡扎菲在执政初期并未遭遇尼克松政府的任何有力反对。华盛顿方面意识到，利比亚的生产者已掌控本国自然资源，而美国仅希望持续获取中东的能源资源。此外，所有西方大国都希望这位年轻的独裁者能成为对抗该地区俄罗斯影响力的堡垒。毕竟，美国企业并未被逐出利比亚，而利比亚市场正日益转向西欧。简言之，每当政策制定者提出对的黎波里持敌对态度的行动方案时，该选项总会因不符合美国利益而被排除。凡此种种表明，尼克松政府正试图“遏制”利比亚政权，同时构筑一道抵御苏联的防线，而苏联正是尼克松“双支柱政策”的真正目标。

如今我们身处大数据革命（Big Data Revolution）时代，网络、社交媒体平台与智能手机中遍布各类数据、信息与知识，共同构筑起庞大的大数据（Big Data）基础。迈尔-舍恩伯格与库克耶在其2013年出版的畅销书《大数据：一场将改变我们生活、工作与思维方式的革命》中指出，得益于互联网、社交网络、智能手机与信用卡的普及，当前针对个人的数据收集与存储规模已达到历史峰值，这为企业与管理者提供了前所未有的机遇——能够以此前完全不可能实现的方式，低成本、高效率地捕获并存储海量数据。在此背景下，能够具备处理数据、从可用数据中挖掘洞见与知识，并利用这些成果实现持续创新、提升企业绩效、开发新产品与服务，乃至建立新型创新商业模式的能力的主体，将成为时代的赢家。

根据麦肯锡（McKinsey）曼尼卡等人（Manyika et al., 2011）开展的一项旨在分析大数据分析对创新、竞争与生产率影响的研究，大数据的应用可通过五大核心路径创造价值：1. 大数据可通过让信息以更高频率实现透明化与可获取性，释放显著价值；2. 随着组织以数字形式创建并存储更多交易数据，它们能够收集从产品库存到病假天数等各类场景下更精准、更详尽的运营信息，从而揭示波动并提升绩效；3. 大数据可实现对客户的愈发精细化的细分，进而打造更为精准定制化的产品或服务；4. 复杂分析技术可大幅优化决策流程；5. 大数据可用于优化下一代产品与服务的开发。

另一方面，大数据的普及也为数据分析与处理带来了全新的时代，助力挖掘未知模式、洞悉客户需求与偏好、贴近客户群体，获取关于其行为与偏好的海量信息，同时识别新的市场趋势与竞争机遇。企业聚合、处理与分析数据的能力正成为关键的竞争优势资源。诸多研究已证实，大数据分析可为企业在创新、效率、生产率、质量与客户满意度等方面带来的价值与成果。

麻省理工斯隆管理评论（MIT Sloan Management Review）与IBM商业价值研究院（IBM Institute for Business Value）联合开展的一项涵盖超3000名企业高管、管理者与分析师的调研显示，高管们正倾向于基于数据驱动的决策开展业务运营，而商业信息与分析技术的应用正是他们在行业中脱颖而出的核心要素（拉瓦勒等人（Lavalle et al., 2010））。此外，用户在Web 2.0平台上生成的内容与信息，是企业直接获取客户偏好与需求的宝贵资产，也是获取直接可靠市场信息的最优质来源。

在此背景下，越来越多的企业正将自身的竞争优势建立在收集、分析数据并基于数据采取行动的能力之上。因此，企业利用数据、分析解读数据以获取洞见，并确保更高效决策流程的能力，已成为创新思维与创造力的核心要素。故而，需将分析能力与创造力相结合，以创新方式解读大数据。

正是基于这一目标，我们策划了本期期刊特刊，旨在发表一系列运用统计与分析模型处理大数据的研究论文，覆盖广泛的议题与行业领域，助力挖掘全新的创新洞见。本期特刊收录了四篇研究论文，聚焦于展示利用大数据获取新洞见与机遇的实践案例。具体如下：
《大数据与知识密集型创业：旅游业的趋势与机遇》一文由德尔韦基奥等人（Del Vecchio et al.）撰写，聚焦于大数据作为宝贵的知识来源，其相关性日益提升，对知识密集型创业的创建与执行产生影响。该文详细阐述了大数据所带来的机遇，并结合旅游领域的实际应用案例，展示了网络中分布的海量知识如何支持更贴合客户需求、聚焦真实市场趋势的创业流程的构思与执行。
卡尔皮塔与西蒙内蒂（Carpita and Simonetti）在《大数据监测大型社会活动：布雷西亚智慧城市中的手机信号分析》一文中，探讨了大数据分析可为市政管理部门规划未来活动、乃至为“智慧城市”制定政策提供的启示。他们运用统计方法处理并评估从手机信号中提取的超大规模数据与信息，以提升城市中举办的大型社会活动的质量，并为区域营销打造可用报告创造条件。
霍布斯等人（Hobbs et al.）撰写的《面向动态数据模型探索的本体论分析（ontological analysis）》一文，探索了数据分析的可扩展方法。作者提出了一种聚合模型，该模型利用本体论工具创建领域模型，支持大数据分析所需的分布式与并行化实现。
《基于网络的应用实现：为诺拉公主大学员工预测最佳培训推荐方案》一文中，穆罕默德与海德（Mohammad and Alhaidey）提出了一款推荐系统（recommender system），可助力企业为员工规划培训课程的决策流程。该推荐系统基于数据挖掘（data mining）技术，能够从大型数据库中挖掘特定模式与知识，并对输出结果进行预测。