To cite this paper: Zhaohao Sun (2018) Imaginational Intelligence: A New Frontier for Innovation, Creativity and Intelligence Development in the Age of Big Data, BAIS No. 1802, PNG University of Technology.
Imaginational Intelligence: A New Frontier for Innovation, Creativity and Intelligence Development in the Age of Big Data Zhaohao Sun, 1 Department of Business Studies PNG University of Technology, Lae 411, PNG
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Abstract. This paper explores imaginational intelligence as a new frontier for innovation, creativity and development in the age of big data. To this end, it examines imaginational intelligence as a fundamental of human intelligence and artificial intelligence. It proposes a lifecycle of imaginational intelligence, and looks at imaginational intelligence for further development of artificial intelligence in the age of big data. It also applies imaginational intelligence to enhance human intelligence, artificial intelligence, and business intelligence in terms of innovation and creativity. The proposed approach in this paper might facilitate the research and development of imaginational intelligence, big data, and business intelligence as well as artificial intelligence. Keywords: imaginational intelligence, big data, artificial intelligence, BI, innovation, creativity, data science. Alternative topic : Imaginational Intelligence: A Fundamental Powerhouse of human intelligence and artificial intelligence Updated based on 5300 words to 8000 words. Dated on 240317, 310317. 020414, 210218
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Introduction
Albert Einstein, one of the greatest thinkers of the 20th century, told us that “The true sign of intelligence is not knowledge but imagination” (Einstein, 2017). Knowledge has been an important topic for many disciplines including big data analytics and data science (Sun, Strang , & Firmin, 2016), artificial intelligence (Russell & Norvig, 2010)and management information systems (Laudon & Laudon, 2016), to name a few. All these consider knowledge is a crucial and strategic asset for any organizations and individuals, especially in the age of big data (McKinsey, 2011), ( Lohr, 2012 February 11), analytics (Henke & Bughin, 2016) and intelligence (Sun & Wang, 2017). However, imagination has not been a topic for consideration in the above mentioned disciplines. We searched “Imagination Intelligence” using Google (on 010417) and found About 72,300 results (0.24 seconds).
This is the era of big data Big data and big data analytics have been revolutionizing innovation, research, development as well as management and business (Chen & Zhang, 2014; Tableau, 2015; McAfee & Brynjolfsson, 2012). Big data analytics services have created big market opportunities. For example, the researcher of IDC (International Data Corporation) forecasts that big data and analytics-related services marketing in Asia/Pacific (Excluding Japan) region will grow from US$3.8 billion in 2016 to US$7.0 billion in 2019 at a 16.3% CAGR (compound annual growth rate) (Roche, 2016). Big data and its emerging technologies including big data analytics have been not only revolutionizing the way the business operates but also making traditional data analytics and business analytics bring new big opportunities for academia and enterprises (Sun, Strang, & Yearwood, 2014; Sun, Zou, & Strang, 2015; McAfee & Brynjolfsson, 2012 ; Reddy, 2014). Big data analytics is an emerging big data technology, and has become a mainstream market adopted broadly across industries, organizations, and geographic regions and among individuals to facilitate big datadriven decision making for businesses and individuals to achieve desired business outcomes (Sun, Firmin, & Yearwood, 2012; Ali, 2016) (Vesset, McDonough, Schubmehl, & Wardley, 2013). The research of Sherrel Roche, IT Services Senior Market Analyst, IDC Asia Pacific reveals that more than half of organizations in the Asia/Pacific (Excluding Japan) region consider big data and analytics important and have adopted or plans to adopt it in the near future (Roche, 2016). Business intelligence (BI) has received increasing attention in academia, business and management since 1989 (Lim, Chen, & Chen, 2013), although it was first introduced by an IBM researcher H.P. Luhn in the 1950s (Luhn, 1958). BI has become not only an important technology for improving business performance of enterprises but also an marketing brand for developing business, e-commerce, e-services (Turban & Volonino, 2011). It is also the momentum for developing organization intelligence, enterprise intelligence, management intelligence and marketing intelligence (Fan, Lau, & Zhao, 2015). However, what does the intelligence mean in BI? This is still an issue for understanding BI completely. Furthermore, BI is facing new big challenges because of dramatic development of big data and big data technologies (Fan, Lau, & Zhao, 2015; Gandomi & Haider, 2015); that is, how to use big data analytics services to enhance BI becomes a big issue for business, e-commerce, e-services, and information systems (Sun, Zou, & Strang, 2015). Big data analytics and BI are the top priorities of chief information officers (CIOs) and comprise a $12.2 billion market (Holsapplea, Lee-Postb, & Pakath, 2014). This fact attracts increasing interest and adoption of big data analytics. According to the annual survey results of 850 CEO and other C-level executives of global organizations, McKinsey (2014) concludes that 45% of executives put “big data and advanced analytics” as the first three strategic priorities in both strategy and spending in three years’ time and more than one thirds of executives will now spend or in three years’ time in this area. IDC predicts that the business analytics software market will grow at a 9.7% compound annual growth rate over the next five years from 2012 to 2017 (Vesset, McDonough, Schubmehl, & Wardley, 2013). The above brief discussion and literature review implies that there is a close relationship between big data analytics and BI. However, the following three important
issues have not been drawn significant attention in the scholarly peer-reviewed literature: • What does the intelligence mean in BI? • What is the relationship between big data analytics and BI? • How can big data analytics services enhance BI? This paper will address these three issues through extending our early research on BI and ontology of big data analytics (Sun, Zou, & Strang, 2015). To address the first issue, we look at the temporality, expectability and relativity of intelligence and consider them as the three dimensions of intelligence of BI, which can be considered as a fundamental for BI including organization intelligence, marketing intelligence (Fan, Lau, & Zhao, 2015) and big data intelligence. To address the second issue, we extend the ontology of big data analytics introduced in our early work through adding a new level, a technological level of big data analytics. To address the third issue, we examine big data analytics as a technology for enhancing BI through examining the relationship between big data analytics and BI. We then review a big data analytics service oriented architecture (BASOA), in which we also explore how to apply big data analytics services to enhance BI, where we show that the proposed BASOA is viable for enhancing BI based on our surveyed data analysis. The remainder of this paper is organized as follows. Section 2 extends an ontology of big data analytics introduced in (Sun, Zou, & Strang, 2015). Section 3 looks at BI and its relationships with big data analytics. It also addresses the above mentioned first issue through examining the temporality, expectability and relativity of intelligence. Section 4 presents BASOA, a big data analytics services-oriented architecture. Section 5 applies proposed BASOA to BI. The final sections discuss the related work and end this paper with some concluding remarks and future work. Big data analytics Big data descriptive analytics
Big data predictive analytics
Big data prescriptive analytics
DM Big data and data analytics Figure 1. An ontology of big data analytics
DW + DM + SM + ML+ Visualization+ Optimization are above Big data and data analytics, where DW, DM, SM and ML are abbreviations of data warehouse, data mining, statistical modelling and machine learning, respectively (Sun, Zou, & Strang,
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2015). The current leading DM includes Amazon’s Redshift, Google’s BigQuery, Microsoft’s Azure SQL Data Warehouse and Teradata (Tableau, 2015). In Figure 1, data analytics refers to as a method or technique that uses data, information, and knowledge to learn, describe and predict something (Turban & Volonino, 2011, p. 341). In brief, data analytics can be then considered as data-driven discoveries of knowledge, intelligence and communications (Delena & Demirkanb, 2013). More generally, data analytics is a science and technology about examining, summarizing, and drawing conclusions from data to learn, describe, predict and visualize something (Sun, Strang, & Yearwood, 2014; Sun, Zou, & Strang, 2015). The fundamentals of big data analytics consist of mathematics, statistics, engineering, human interface, computer science and information technology (Sun, Strang, & Yearwood, 2014; Chen & Zhang, 2014). The techniques for big data analytics encompass a wide range of mathematical, statistical, and modeling techniques (Coronel & Morris, 2015, p. 590). Big data analytics always involves historical or current data (often related to operations) and visualization (Sun & Yearwood, 2014). This requires big data analytics to use data mining (DM) to discover knowledge from a data warehouse (DW) or a big dataset in order to support decision making, in particular in the text of big business and management (Turban & Volonino, 2011, p. 344). DM employs advanced statistical tools to analyze the big data available through DWs and other sources to identify possible relationships, patterns and anomalies and discover information or knowledge for rational decision making (Coronel & Morris, 2015, p. 590; Kantardzic, 2011). DW extracts or obtains its data from operational databases as well as from external open sources, providing a more comprehensive data pool including historical or current data (Coronel & Morris, 2015, p. 590). Big data analytics also uses statistical modelling (SM) to knowledge and wisdom through descriptive analysis that can support decision making (Sun, Zou, & Strang, 2015). Visualization technologies including display technologies as an important part of big data analytics make knowledge patterns and information for decision making in a form of figure or table or multimedia. In summary, big data analytics in general and big descriptive data analytics, big predictive data analytics, big prescriptive data analytics in specific can facilitate business decision making and realization of business objectives through analyzing current problems and future trends, creating predictive models to forecast future threats and opportunities, and analyzing/optimizing business processes based on involved historical or current data to enhance organizational performance using the mentioned techniques (Delena & Demirkanb, 2013). Therefore, big data analytics can be represented below (Sun, Zou, & Strang, 2015). Big data analytics = Big data + data analytics + DW + DM + SM + ML+ Visualization+ optimization
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Equation 2 reveals the fundamental relationship between big data, data analytics and big data analytics, that is, big data analytics is based on big data and data analytics, as illustrated in Figure 1. It also shows that computer science and information technology play a dominant role in the development of big data analytics through providing sophisticated techniques and tools of DM, DW, ML and visualization (Sun, Strang, & Yearwood, 2014). SM and optimization still plays a fundamental role in the
development of big data analytics, in particular in big data prescriptive analytics (Minelli, Chambers, & Dhiraj, 2013). It should be noted that Equation 2 is a concise representation for the technological components of big data analytics whereas the proposed ontology of big data analytics in this Section is to look at what big data analytics constitutes at a relatively high level, also see Equation 1. At a relatively lower level, the ontology also illustrates what technologies and techniques can support big descriptive data analytics, big predictive data analytics, big prescriptive data analytics, as illustrated in Figure 1. Apache Hadoop is a platform of big data analytics (Reddy, 2014). As an open source platform for storing and processing large datasets using clusters and commodity hardware, Hadoop can scale up to hundreds and even hundreds of nodes. Apache Spark is one of the most popular big data analytics services. It has moved from being a component of the Hadoop system, to the big data analytics platform for a number of enterprises (Tableau, 2015; Reddy, 2014). Spark provides dramatically increased large-scale data processing compared to Hadoop, and a NoSQL database for big data management (Coronel & Morris, 2015; Reddy, 2014). Apache Spark has provided Goldman Sachs with excellent big data analytics services (Tableau, 2015). We will consider the big data descriptive, predictive and prescriptive analytics as one dimension, and the technological components of big data analytics as another dimension. Then we will provide this 2-dimension analysis as a future research work.
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Imaginational Intelligence: A Fundamental Perspective
Imaginational intelligence is the capability or skill of individuals to react and reflect what they listen, read, speak and hear to form, perceive and create new ideas, methods, and pictures instantly or effectively to solve a problem and improve a decision making (Bhardwaz , 2016). [Sun 01 04 17]. Association is an important attribute for Imaginational intelligence. One can associate A with B that he or she just read, listen, read and speak critically, and rationally. For example, when Peter reads a paper on fuzzy reasoning (A), he has an idea that fuzzy reasoning looks like to similarity-based reasoning. This is the first step of imaginational intelligence. Imaginational intelligence is at least as important as emotional intelligence and IQ for success, including in academic, professional, social, and interpersonal aspects of one's life (Goleman, 1995). It is a skill that can be taught and cultivated either in school or in society. Imaginational intelligence might be more important than emotional intelligence and IQ for one’s intelligence development, at least it is the fundamentals for developing emotional intelligence and IQ. It is the original force for one’ skill of innovation and creation. Hegel has studied the relationship between imagination and intelligence. He has referred imagination as a kind of intelligence which wields the stores of images and ideas belonging to it. [Sun 010417]
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Fundamentals of Intelligence
This section examines fundamentals of intelligence (BI) and its evolution. It also addresses its applications in human intelligence, artificial intelligence and business intelligence intelligence (BI). 3.1
Business Intelligence
There are many different definitions on BI from different perspectives. For example, • BI is a framework that allows a business to transform data into information, information into knowledge, and knowledge into wisdom (Coronel & Morris, 2015, p. 560). BI has the potential to positively affect a company's culture by creating “business wisdom” and distributing it to all users in an organization. This business wisdom empowers users to make sound business decisions based on the accumulated knowledge of the business as reflected on recorded historic operational data (Coronel & Morris, 2015, p. 560). • BI refers to as a collection of information systems (IS) and technologies that support managerial decision makers of operational control by providing information on internal and external operations (Turban & Volonino, 2011). • BI is defined as providing decision makers with valuable information and knowledge by leveraging a variety of sources of data as well as structured and unstructured information (Sabherwal & Becerra-Fernandez, 2011). The first definition of BI emphasizes that BI is a framework and creates business wisdom for decision makers through business data, information and knowledge and their transformations. The second definition stresses “a collection of ISs and technologies” while specifies the decision makers to “managerial decision makers of operational control”, and information to “information on internal and external operations”. The last definition emphasizes BI “providing decision makers with valuable information and knowledge”. Based on the above analysis, BI can be defined as a framework that consists of a set of theories, methodologies, architectures, systems and technologies that support business decision making with valuable data, information, knowledge and wisdom. This definition reflects the evolution of BI and its technologies from decision support systems (DSS) and its relations with data warehouses, executive information systems (Holsapplea, Lee-Postb, & Pakath, 2014). The principal tools for BI include software for database query and reporting (e.g. SAP ERP, Oracle ERP, etc.), tools for multidimensional data analysis (e.g. OLAP), and DM e.g. predictive analysis, text mining, web mining (Laudon & Laudon, 2016). DM is also considered as a foundation of BI (Lim, Chen, & Chen, 2013). 3.2
Temporality, Expectability and Relativity of BI
The widespread development of BI in the past about three decades in the business world has so far ignored a major issue: What does the intelligence mean in BI? In what
follows, we will address this issue through examining temporality, expectability and relativity of BI. As the name implies, BI is to create intelligence about a business. This intelligence is based on learning and understanding the facts provided by business data, information and knowledge about a business environment (Coronel & Morris, 2015). The ability of learning, understanding and reasoning belong to the category of intelligence (Wang, 2012 ). The term “intelligent” has been popular, not only in academia but also in the wider community, due to a long time, ongoing research and development of artificial intelligence (AI) and intelligent systems (IS) since 1955 (Russell & Norvig, 2010). There are about 243 million results related to “intelligent” in the Google world (searched on 27 may 2016). In the academia, the term “intelligent” frequently appears in titles of a great number of books, book chapters, papers, and international conferences as well as other media or products. In the wider community, the term “intelligent” often appears in home appliances and customer electronics including televisions, cameras, vacuum cleaners, washing machines (Sun, Zou, & Strang, 2015), and mobile phones, to name a few. Defining intelligent is not a simple question. According to the Macmillan Dictionary (2007, p. 787), the term intelligence means “the ability to understand and think about things, and to gain and use knowledge”. Similarly, the term intelligence has been defined in IS as “the ability to learn and understand, solve problems and make decision” (Negnevitsky, 2005, p. 18). The term intelligent means to be able to perceive, understand, think, learn, predict and manipulate a system (Russell & Norvig, 2010, p. 1). All these definitions on intelligence are mainly human intelligence, which has impacted the development of AI (Russell & Norvig, 2010). AI has been focusing on intelligence of machines or machine intelligence (Note that the web is also a machine.). In other words, AI is the science and engineering of making intelligent machines (Wang, 2012). However, a system may not be considered intelligent, even if it has these abilities associated with human intelligence, because the term intelligent implies some expectations from human beings or society. Practically, it appears that an intelligent system contains a set of functions that jointly make the system easy to use (Astrom & McAvoy, 1992), because ‘easy’ is a term related to human intelligence. More generally, a system or a product is intelligent if and only if it contains a set of functions that jointly make the system either easier or faster, or friendlier, or more efficient, or more satisfactory to use than an existing cognate system taking into account the time. Here easier, faster, or friendlier, or more efficient, or more satisfactory are all the expectations of humans or customers or society for the performance of a system or product. For example, a high speed train running in China is intelligent, because it is faster and friendlier, these are what the Chinese expect. The above consideration leads to three perspectives on “intelligence”. Firstly, term intelligence is temporal, or temporality of intelligence. There are two meanings for temporal intelligence. 1. Temporal intelligence is the ability to adapt to change. This has been motivated to develop temporal logic and evolutionary computing including genetic algorithms (Russell & Norvig, 2010). 2. Temporality of intelligence means that intelligence is related or limited to a time interval. For example, at the time of writing this paper, few people consider floppy disks as intelligent storage devices. However, a few decades ago floppy disks were considered intelligent in comparison to paper tape for data storage. Another story is that a pupil was only six years old.
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However, he could do calculus very well. The other classmates said that he was intelligent. However, that pupil dropped out when he was in Year 5. These two stories reflect the temporality of intelligence. In what follows, we limit ourselves to the meaning of item 2. Secondly, term intelligence can be considered as a substitution for easier, or faster, or friendlier, or more efficient, or more satisfactory. This is expectability of intelligence. We denote them using the degree of satisfaction. All these related concepts are a set of expectations of humans, as parts of human intelligence. We denote these expectations for a product as 𝐸𝑃 = {𝑒𝑖 |𝑒𝑖 𝑖𝑠 𝑎𝑛 𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 𝑓𝑜𝑟 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑖 𝑜𝑓 𝑎 𝑝𝑟𝑜𝑑𝑢𝑐𝑡 } = {𝑒𝑖 |𝑖 ∈ {1, 2, … , 𝑛 − 1, 𝑛} , where P is for a product, n is a fixed integer. For every 𝑖 ∈ {1, 2, … , 𝑛 − 1, 𝑛}, there is a perceived performance of customer for functioni 𝑝𝑖 , then a product P is intelligent if and only if there exists at least one 𝑖 ∈ {1, 2, … , 𝑛 − 1, 𝑛} such that (Larson & Gray, 2011, p. 436) 𝑝𝑖 𝑠𝑖 = ≥0 𝑒𝑖 where 𝑠𝑖 is the satisfaction degree of the customer to the i function of product P. For example, an iPhone 6S is intelligent, because its Touch ID, Apple’s fingerprint recognition feature, is noticeably quicker when unlocking the phone. “quicker” is what the user perceived, e.g. 𝑝1 , while “quick” is an expected performance for iPhone 6S from a customer (http://www.trustedreviews.com/iphone-6-review-performance-page3, retrieved on 28 Apr 16). Thirdly, intelligence is a consequence of comparison between two systems, which leads to the relativity of intelligence. Generally speaking, let X and Y be two systems. X is intelligent if X is better than Y with respect to E, where E is a set of human expectations. “Better” is a relativity concept based on comparison. For example, a new microwave is intelligent because it displays the temperature when microwaving food. A user believes that displaying the temperature is better than not displaying it. This example reflects the relativity of intelligence. This is the third dimension that intelligence is relative. Displaying temperature belongs to the set of expectations E. The set of human expectations can be considered as a set of demands. The expectation of human beings and society promotes intelligence and social development. Therefore, it is significant to define IS with respect to the set of human expectations or demands. In summary, intelligence, in general and intelligence in BI can be measured through three dimensions: Temporality, Expectability and Relativity. In other words, in any BI system there are three characteristics of its intelligence: Temporality, Expectability and Relativity. The degree of intelligence of a BI system or product can be measured using this triad, that is, Degree of intelligence = temporality+ expectability+ relativity 3 Equation 3 is more useful for BI and big data intelligence (for short, big intelligence), because BI is based on performance, business advantages, competiveness advantages of systems or products. This formula can be realized by using big data analytics and big data, in other words, big data and big data analytics can generate big intelligence, for short, big intelligence = big data +big data analytics 4
Equation 4 indicates that either increase of either big data or big data analytics can increase the degree of intelligence of big intelligence. This is partially proved by what Professor Peter Norvig, the Google’s Director of Research, said that “we don’t have better algorithms; we just have big data” (McAfee & Brynjolfsson, 2012 ). In fact, the global competiveness among the giant companies lies in these three dimensions of intelligence in businesses, decision making, products and systems. Big data and analytics will intensify the competition of the giant companies in terms of temporality, expectability, relativity of intelligence. This degree of intelligence also differentiates BI from AI using three properties of its intelligence in BI. Throughout this paper, we use these three dimensions of intelligence as the basis to understand BI. Cloud services
Mobile services Big data analytics services e-services
Social networking services
Figure 2. Interrelationship between big data analytics services and web services
It should be noted that for the state-of-art web services, Sun et al (Sun & Yearwood, 2014; Sun, Zou, & Strang, Big Data Analytics as a Service for Business Intelligence , 2015) explores that web services mainly consist of mobile services, analytics services, cloud services, social networking services, and service as a web service. Here we emphasize big data analytics services at the center to support cloud services, social networking services, mobile services, e-services to reflect the big data and big data analytics as an emerging new service (Sun, Zou, & Strang, 2015). Based on IDC’s prediction for the IT market in 2014 (IDC, 2013), spending on big data will explode and grow by 30%, to $14+ billion, in which, the spending on big data analytics services will exceed $4.5 billion, growing 21%. The number of providers of big data analytics services will triple in three years. This means that big data analytics services have become an important emerging market, together with the Internet of services including e-services, cloud services, mobile services and social networking services. All these five services and the technologies shape the most important markets for e-commerce and e-business (Sun & Yearwood, 2014). Furthermore, BI is a more general concept for improving business performance and business decision making. Big data analytics is a pivotal part for developing BI, at least from a technological viewpoint and data viewpoint. From a technological viewpoint, big data analytics is big data-driven and business-oriented technology and facilitates business decision making and then improves BI (Sun, Strang, & Yearwood, 2014; Sun, Zou, & Strang, 2015). From a data viewpoint, big data analytics relies on data analytics and big data which have become a strategic natural resource for every organization, in particular for multinational organizations as well as for e-commerce and e-services. Discovering information, knowledge and wisdom from databases, data warehouses,
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data marts and the Web has become the central topics for business operations, marketing and BI (Sun, Zou, & Strang, 2015). This is just the task of big data analytics. Big data analytics service broker Publish Big data analytics service provider
Find
Bind
Big data analytics service requestor
Figure 3. BASOA: A big data analytics SOA
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A Lifecycle of Imaginational Intelligence: A Process-Oriented Perspective
Association, search, generalize, and specialize are all the key for imaginational intelligence
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Imaginational Intelligence in the Age of Big Data 020417
5.1
Search and Hyperlink
Search and hyperlink is a kind of realization of imaginational intelligence. It is also a kind of engineering of imaginational intelligence. For search, when I read an article, I image an idea X is new, then I have to search on the web to know if X is a new idea. If X is available on the Web, how is different my idea from the existing idea. This research process starts from the search, which is motivated from the imagination when reading an article. In the age of big data, one does not know a lot before of intelligence limitation and memory limitation of the brain. The search is an extension of intelligence and memory limitation. 5.2
Generalization and specialization
020417 generalization, and specialization are all the key for imaginational intelligence. Some are good at generalization, while others are good at specialization, the former might become a leader, while the latter might become an team member. Therefore, generalization, and specialization are skill of imaginational intelligence
5.3
Association
Association is a kind of imaginational intelligence. Association from one world to another, from a small world to a big one. Hyperlink is a kind of association realization.
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Imaginational Intelligence: A motive to any Innovation
Imagination intelligence allows us to take data, information, knowledge and apply them in the frontier of innovation, creativity and intelligence development in the age of big data.
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Applying imaginational intelligence to Enhance HI Learning is a relatively simple intelligence of human beining, because the core of learning is to imitate what others have done and what others said and what others written. Learning is a basic function of human intelligence to copy the past and store what one learned from the past to one’s head or brain.
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Applying imaginational intelligence to Enhance BI
This section looks at how to apply the proposed BASOA to enhance BI in some detail. Analytics as a service (AaaS) is a relatively new concept that has emerged as a rapidly growing business sector of web analytics industry, which provides efficient web log analytic services for firm-level customers (Sun & Yearwood, 2014). BAaaS (Big data analytics as a service), as discussed in the BASOA above, means that an individual or organization or information system or software agent uses a wide range of analytic tools or apps wherever they may be located (Delena & Demirkanb, 2013). BAaaS has the ability to turn a general analytic platform into a shared utility for an enterprise or organization with visualized analytic services (Delena & Demirkanb, 2013). A big data analytics service can be available on the Web or used by smartphone. Therefore, big data analytics services include e-analytics services or web analytics services (WAS) and Amazon Web Services (AWS) (Sun, Strang, & Yearwood, 2014; Tableau, 2015). Furthermore, big data analytics services also include business analytics services, marketing analytics services, organizational analytics services, security analytics services and predictive analytics (Roche, 2016). Big data analytics services are gaining popularity rapidly in business, e-commerce, e-service, and management in recent years. For example, Big data analytics services model has been adopted by many famous web companies such as Amazon, Microsoft, and eBay (Delena & Demirkanb, 2013). The key reason behind it is that the traditional hub-and-spoke architectures cannot meet the demands driven by increasingly complex business analytics (Delena & Demirkanb, 2013). BAaaS promises to provide decision makers with visualizing much needed big
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data. Cloud analytics is an emerging alternative solution for big data analytics (Sun, Strang, & Yearwood, 2014). As previously defined, BI is a set of theories, methodologies, architectures, systems and technologies that support business decision making with valuable data, information and knowledge”. BASOA is an architecture for supporting business decision making with big data analytics services. The theory of big data analytics providers, brokers and requestors of the BASOA can facilitate the understanding and development of BI and business decision making. For example, from an in-depth study of the BASOA, an enterprise and its CEO can know who are the best big data analytics providers and brokers in order to improve his organization, business, market performance, and global competitiveness. We surveyed 71 information technology managers at the Association for Education in Journalism and Mass Communication (AEJMC) in Montreal during August 6-9, 2014 (Sun, Strang, & Yearwood, 2014), to collect data concerning the enterprise-level acceptability of the BASOA concept. These results indicate some preliminary support for the BASOA concept of having service brokers work with service requesters and providers similar to the way private mortgage and loans work in the USA. Based on this preliminary enterprise acceptability of this BASOA model, we propose that more research be done to investigate how it could be used.
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Applying imaginational intelligence to Enhance AI
Based on the analysis of the previous section, machine learning aims to copy what it stored in the knowledge base, or existing knowledge base, although it is open. For example, if we consider the Web as a knowledge base, then it is open and scalable. The another function of machine learning is similarity-based reasoning or problem solving (Sun, Finnie, & Weber, 2004), even in deep learning.
10 11 Related Work and Discussion Updated on 01 04 17 We have mentioned a number of scholarly researches on data analytics, big data analytics, and BI. In what follows, we will focus on related work and discussion on ontology of big data analytics, and the work of SAP as well as incorporation of big data analytics into BI. We searched “Imagination Intelligence” using Google (on 010417) and found About 72,300 results (0.24 seconds). However we searched “Imaginational Intelligence” using Google (on 010417) and found zero results. We use imaginational intelligence motivated from “emotional intelligence” (Wikipedia, 2017), it has not used “emotion intelligence”.
Bhardwaz considers imagination as more important than intelligence (Bhardwaz , 2016). He asks where would we have been now? if human have been gifted only with intelligence rather than imagination. Obviously, he differentiate imagination from human intelligence. In this article we consider imagination intelligence as a kind of human intelligence. Intelligence not only include acquiring knowledge (Bhardwaz , 2016), but also imagination. Further, imaginational intelligence usually associate with human behaviors including reading, seeing, listening and writing and doing more generally. Why does big data analytics really matter for modern business organizations? There are many different answers to this question from different researchers. For example, Davis considers that the current big data analytics has embodied the state-of-art current development of modern computing (Davis, 2014), which has been reflected in Section 2. Gandomi and Harder (2015) discuss how big data analytics has captured the attention of business and government leaders through decomposing big data analytics into text analytics, audio analytics, video analytics, social media analytics, and predictive analytics. This implies that big data has been classified into big text data, big audio data, big video data, and big social media data in (Gandomi & Haider, 2015). Big data analytics and BI have drawn an increasing attention in the computing, business, and e-commerce community. For example, Lim et al (2013) examine BI and analytics by focusing on its research directions. They consider BI and analytics (BIA) as a current form replacing the traditional BI, whereas we still consider BI and big data analytics are two different concepts, although they have close relationships and share some commons. Fan et al (Fan, Lau, & Zhao, 2015) provide a marketing mix framework for big data management through identifying the big data sources, methods, and applications for each of the marketing mix, consisting of people, product, place, price and promotion. However, what is the relationship between marketing intelligence and BI in terms of big data analytics should have been mentioned in their work (Fan, Lau, & Zhao, 2015). Ontology has been important in computer science and AI (Gruber, 1995). A basic search in Google scholar (i.e. article title and key words) reveals that there are few publications entitled “ontology of big data analytics”. We then explored it and put it as a part of this research through updating our early work on data analytics, business analytics and big data analytics (Sun, Strang, & Yearwood, 2014; Sun, Zou, & Strang, 2015). Comparing with the early proposed ontology of big data analytics in (Sun, Zou, & Strang, 2015), the proposed ontology of big data analytics in this paper, illustrated in Figure 1, includes a technology level consisting of DW + DM + SM + ML+ Visualization+ Optimization, all these reflect the advanced progress of ICT in general and in DW, DM, SM, ML, Visualization and Optimization in specific. Incorporating these advanced techniques to data analytics for big data is the essence of big data analytics (Sun, Zou, & Strang, 2015). More specifically, this added level arms big data descriptive, predictive and prescriptive analytics with these advanced techniques in ICT. This is only a beginning for providing a relatively comprehensive ontology of big data analytics. In this direction, we will investigate more academic reviewed sources as a future work to develop an ontology of big data analytics with three levels for each related analytics: big data, methods and applications based on the method of Fan et al
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(Fan, Lau, & Zhao, 2015). Such an investigation would become an important guide for the research and development of big data analytics. SAP, one of the leading vendors of ERP (Elragal, 2014), has introduced its enterprise service-oriented architecture (ESOA) (Laudon & Laudon, 2016). SAP’s ESOA specifies general services to enterprise services whereas our BASOA model specifies general services to big data analytics services. Big data analytics services should be a part of state-of-the-art e-commerce and e-services (Sun & Yearwood, 2014), and then the proposed BASOA can be considered as a concrete application for the ESOA of SAP. However, SAP’s enterprise systems focus on key applications in finance, logistics, procurement and human resources management as an ERP system. We conceive that our BASOA will be incorporated into the next generation enterprise systems integrating SCM, CRM, and KM systems, and e-commerce systems. This is also the motivation of our proposed BASOA.
12 Conclusion This paper examined how to use big data analytics services to enhance BI by presenting an ontology of big data analytics and a big data analytics services-oriented architecture (BASOA), and then applying BASOA to BI, where our surveyed data analysis showed that the proposed BASOA is viable for enhancing BI and enterprise information systems. This paper also examined temporality, expectability and relativity as the characteristics of intelligence in BI, and discussed the interrelationship between BI and big data analytics. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, BI, e-commerce, and e-services as well as big data computing in general and big data science in specific. In the future work, besides mentioned in the previous sections, we will analyse the foregoing collected data vigorously and explore enterprise and e-commerce acceptability of BASOA for BI. We will also realize BASOA using intelligent agents technology (Sun & Finnie, 2004; 2010), where we will also look at some implementation related issues such as how to collect, store, and process big data – by whom, for what, access rights, and many more. We will provide significant examples for modeling temporality, expectability and relativity of BI using big data analytics such as Google Analytics.
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13 Diary II is a topic in my head since I published a few papers in JCIS in 2016 and 2017. On 24 03 17, I attended the presentation on emotional intelligence and I associated it with my II using a mathematical equation and showed my VC, Dr Schram, who sat by me at that time. On 30 03 17, I was invited to deliver a presentation to students and staff at PNG UoT, I have to draft a presentation slides on innovation and entrepreneauership. I found that II is the motive of any innovation, and then I mentioned it in the presentation on 31 03 17. 210218 Today I have paid attention to
Sridhar Mahadevan, Imagination Machines: A New Challenge for Artificial Intelligence today from CCC news, Blue Sky Ideas Conference Track at AAAI-18 from
[email protected], 21 Feb 2018 . I will get it and read it. Maybe we share some commons on imaginational intelligence.
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