Social media competitive analysis and text mining: A case study in the pizza industry

The advances in cloud computing and internet of things (IoT) have provided a promising opportunity to resolve the challenges caused by the increasing transportation issues. We present a novel multilayered vehicular data cloud platform by using cloud computing and IoT technologies. Two innovative vehicular data cloud services, an intelligent parking cloud service and a vehicular data mining cloud service, for vehicle warranty analysis in the IoT environment are also presented. Two modified data mining models for the vehicular data mining cloud service, a Naive Bayes model and a Logistic Regression model, are presented in detail. Challenges and directions for future work are also provided.

Developing Vehicular Data Cloud Services in the IoT Environment

The demand for artificial intelligence has grown significantly over the past decade, and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, to increase the quality of predictions and render machine learning solutions feasible for more complex applications, a substantial amount of training data is required. Although small machine learning models can be trained with modest amounts of data, the input for training larger models such as neural networks grows exponentially with the number of parameters. Since the demand for processing training data has outpaced the increase in computation power of computing machinery, there is a need for distributing the machine learning workload across multiple machines, and turning the centralized into a distributed system. These distributed systems present new challenges: first and foremost, the efficient parallelization of the training process and the creation of a coherent model. This article provides an extensive overview of the current state-of-the-art in the field by outlining the challenges and opportunities of distributed machine learning over conventional (centralized) machine learning, discussing the techniques used for distributed machine learning, and providing an overview of the systems that are available.

/pdf/a-survey-on-distributed-machine-learning-29oanail6a.pdf

A Survey on Distributed Machine Learning

The information systems (IS) literature has long emphasized the positive impact of information provided by business intelligence systems (BIS) on decision-making, particularly when organizations operate in highly competitive environments. Evaluating the effectiveness of BIS is vital to our understanding of the value and efficacy of management actions and investments. Yet, while IS success has been well-researched, our understanding of how BIS dimensions are interrelated and how they affect BIS use is limited. In response, we conduct a quantitative survey-based study to examine the relationships between maturity, information quality, analytical decision-making culture, and the use of information for decision-making as significant elements of the success of BIS. Statistical analysis of data collected from 181 medium and large organizations is combined with the use of descriptive statistics and structural equation modeling. Empirical results link BIS maturity to two segments of information quality, namely content and access quality. We therefore propose a model that contributes to understanding of the interrelationships between BIS success dimensions. Specifically, we find that BIS maturity has a stronger impact on information access quality. In addition, only information content quality is relevant for the use of information while the impact of the information access quality is non-significant. We find that an analytical decision-making culture necessarily improves the use of information but it may suppress the direct impact of the quality of the information content.

https://nipc.ir/uploads/intelligence_systemsT004_15563.pdf

Towards business intelligence systems success: Effects of maturity and culture on analytical decision making

Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, recent improvements in the field of parallel programming already provide good tools to tackle this problem. However, these tools come with their own technical challenges, e.g. balanced data distribution and inter-communication costs. In this paper, we investigate the applicability of FIM techniques on the MapReduce platform. We introduce two new methods for mining large datasets: Dist-Eclat focuses on speed while BigFIM is optimized to run on really large datasets. In our experiments we show the scalability of our methods.

Frequent Itemset Mining for Big Data

Business intelligence (BI) has been viewed as sets of powerful tools and approaches to improving business executive decision-making, business operations, and increasing the value of the enterprise. The technology categories of BI mainly encompass data warehousing, OLAP, and data mining. This article reviews the concept of Business Intelligence and provides a survey, from a comprehensive point of view, on the BI technical framework, process, and enterprise solutions. In addition, the conclusions point out the possible reasons for the difficulties of broad deployment of enterprise BI, and the proposals of constructing a better BI system.

http://sourcedb.ict.cas.cn/cn/ictthesis/200907/P020090722622668182276.pdf

Techniques, Process, and Enterprise Solutions of Business Intelligence

Most data mining approaches assume that the data can be provided from a single source. If data was produced from many physically distributed locations like Wal-Mart, these methods require a data center which gathers data from distributed locations. Sometimes, transmitting large amounts of data to a data center is expensive and even impractical. Therefore, distributed and parallel data mining algorithms were developed to solve this problem. In this paper, we survey the-state-of-the-art algorithms and applications in distributed data mining and discuss the future research opportunities.

Distributed data mining: a survey

Centralized data mining techniques are widely used today for the analysis of large corporate and scientific data stored in databases. However, industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed systems. The Grid can play a significant role in providing an effective computational infrastructure support for this kind of data mining. Similarly, the advent of multi-agent systems has brought us a new paradigm for the development of complex distributed applications. During the past decades, there have been several models and systems proposed to apply agent technology building distributed data mining (DDM). Through a combination of these two techniques, we investigated the critical issues to build DDM on Grid infrastructure and design an Agent Grid Intelligent Platform as a testbed. We also implement an integrated toolkit VAStudio for quickly developing agent-based DDM applications and compare its function with other systems.

http://sourcedb.ict.cas.cn/cn/ictthesis/200907/P020090722620056527951.pdf

Distributed data mining on agent grid: issues, platform and development toolkit

Business intelligence (BI) is the process of gathering enough of the right information in the right manner at the right time, and delivering the right results to the right people for decision-making purposes so that it can continue to yield real business benefits, or have a positive impact on business strategy, tactics, and operations in the enterprises. This paper was intended as a short introduction to the study of business intelligence in enterprise computing environment. In addition, the conclusions point out the challenges to broad and deep deployment of business intelligence systems, and provide the proposals of making business intelligence more effective.

Research on Business Intelligence in enterprise computing environment

In this paper we propose dynamic description logic for formalizing multi-agent cooperation process with a clearly defined syntax and semantics. By combining the features of knowledge representation and reasoning of description logic and action theory for multi-agent interaction, our logic is effective and significant both for static and dynamic environment. On the static side, we employ description logic for the representation and reasoning of beliefs and goals. On the dynamic side, we adopt the object-oriented method to describe actions. The description of each action is composed of models, preconditions and effects. It can reflect the real changes of the world and is very suitable for belief revision and action planning. Based on our logic, we investigate how to form joint goal for multi-agent cooperation. In particular, we propose an effective dynamic planning algorithm for scheduling sub goals, which is greatly crucial for coordinating multi-agent behaviors.

http://sourcedb.ict.cas.cn/cn/ictthesis/200907/P020090722599076378489.pdf

Maoguang Wang

Papers

Techniques, Process, and Enterprise Solutions of Business Intelligence

Distributed data mining: a survey

Distributed data mining on agent grid: issues, platform and development toolkit

Research on Business Intelligence in enterprise computing environment

Multi-agent Cooperation: A Description Logic View