Business Intelligence, Analytics and Cognitive: Case Studies and Applications (COGS)

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    The Challenges of Business Analytics: Successes and Failures
    ( 2018-01-03) Liu, YIng ; Han, Henry ; DeBello, Joan
    The successful use of business analytics is an important element of a company’s success. Business analytics enables analysts and managers to engage in an IT-driven sense-making process in which they use the data and analysis as a means to understand the phenomena that the data represent". Not all organizations apply business analytics successfully to decision making. When used correctly, the actionable intelligence gained from a business analytics program can be utilized to improve strategic decision making. Conversely, an organization that does not utilize business analytics information appropriately will not experience optimal decision making; failing to realize the full potential of a data analytics program. This paper examines some organizations that implemented data analytics programs; both successfully and unsuccessfully, and discuss the implications for each organization. Based on the lesson learned, we present ways to implement a successful business analytics program.
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    Customization of IBM Intu’s Voice by Connecting Text-to-Speech Services and a Voice Conversion Network
    ( 2018-01-03) Song, Jongyoon ; Kim, Hyunjae ; Lee, Jaekoo ; Choi, Euishin ; Kim, Minseok ; Yoon, Sungroh
    IBM has recently launched Project Intu, which extends the existing web-based cognitive service Watson with the Internet of Things to provide an intelligent personal assistant service. We propose a voice customization service that allows a user to directly customize the voice of Intu. The method for voice customization is based on IBM Watson’s text-to-speech service and voice conversion model. A user can train the voice conversion model by providing a minimum of approximately 100 speech samples in the preferred voice (target voice). The output voice of Intu (source voice) is then converted into the target voice. Furthermore, the user does not need to offer parallel data for the target voice since the transcriptions of the source speech and target speech are the same. We also suggest methods to maximize the efficiency of voice conversion and determine the proper amount of target speech based on several experiments. When we measured the elapsed time for each process, we observed that feature extraction accounts for 59.7% of voice conversion time, which implies that fixing inefficiencies in feature extraction should be prioritized. We used the mel-cepstral distortion between the target speech and reconstructed speech as an index for conversion accuracy and found that, when the number of target speech samples for training is less than 100, the general performance of the model degrades.
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    Meeting Analytics: Creative Activity Support Based on Knowledge Discovery from Discussions
    ( 2018-01-03) Nagao, Katashi
    We are researching a mechanism to promote innovation by supporting discussions based on the premise that innovation results from discussions. Ideas are created and developed mainly by conversations in creative meetings like those in brainstorming. Ideas are also refined in the process of repeated discussions. Our previous research called discussion mining was specifically used to collect various data on meetings (statements and their relationships, presentation materials such as slides, audio and video, and participants’ evaluations on statements). We extracted important statements to be considered especially after the meetings had been held and actions had been undertaken, such as investigations and implementations that were performed in relation to these statements by using the collected data. Here, we present high-probability statements that should lead to innovations during meetings and facilitate creative discussions. We also propose a creative activity support system that should help users to discover and execute essential tasks.
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