Abstract: Digital technology is transforming the landscape of the industrial sector at a precedential level by connecting people, processes, and machines in real-time. It represents the means for a new pathway to achieve innovative, dynamic competitive advantages, deliver unique customers’ values, and sustain critical relationships. Thus, success in a constantly changing environment is governed by the ability of an organization to revolutionize their business models, deliver innovative solutions, and capture values from big data analytics and insights. Businesses need to re-strategize operations and develop extra capabilities to cope with the necessity for additional flexibility and agility. The traditional “command and control” leadership style is structurally and operationally incompatible with the digital era. In this paper, the authors discuss how transformational leaders can act as a glue in the social, organizational context, which is crucial to enable the workforce and develop a psychological attachment to the digital vision.
Abstract: Surface Mount Technology (SMT) is widely used in the area of the electronic assembly in which the electronic components are mounted to the surface of the printed circuit board (PCB). Most of the defects in the SMT process are mainly related to the quality of solder paste printing. These defects lead to considerable manufacturing costs in the electronics assembly industry. Therefore, the solder paste inspection (SPI) machine for controlling and monitoring the amount of solder paste printing has become an important part of the production process. So far, the setting of the SPI threshold is based on statistical analysis and experts’ experiences to determine the appropriate threshold settings. Because the production data are not normal distribution and there are various variations in the production processes, defects related to solder paste printing still occur. In order to solve this problem, this paper proposes an online machine learning algorithm, called the automatic threshold adjustment (ATA) algorithm, and closed-loop architecture in the SMT process to determine the best threshold settings. Simulation experiments prove that our proposed threshold settings improve the accuracy from 99.85% to 100%.
Abstract: Big Data (BD) is associated with a new generation of technologies and architectures which can harness the value of extremely large volumes of very varied data through real time processing and analysis. It involves changes in (1) data types, (2) accumulation speed, and (3) data volume. This paper presents the main concepts related to the BD paradigm, and introduces architectures and technologies for BD and BD sets. The integration of BD with the Hadoop Framework is also underlined. BD has attracted a lot of attention in the public sector due to the newly emerging technologies that allow the availability of network access. The volume of different types of data has exponentially increased. Some applications of BD in the public sector in Romania are briefly presented.
Abstract: Policy makers are increasingly looking to make evidence-based decisions. Evidence-based decisions have historically used rigorous methodologies of empirical studies by research institutes, as well as less reliable immediate survey/polls often with limited sample sizes. As we move into the era of Big Data analytics, policy makers are looking to different methodologies to deliver reliable empirics in real-time. The question is not why did these people do this for the last 10 years, but why are these people doing this now, and if the this is undesirable, and how can we have an impact to promote change immediately. Big data analytics rely heavily on government data that has been released in to the public domain. The open data movement promises greater productivity and more efficient delivery of services; however, Australian government agencies remain reluctant to release their data to the general public. This paper considers the barriers to releasing government data as open data, and how these barriers might be overcome.
Abstract: The system for analyzing and eliciting public
grievances serves its main purpose to receive and process all sorts of
complaints from the public and respond to users. Due to the more
number of complaint data becomes big data which is difficult to store
and process. The proposed system uses HDFS to store the big data
and uses MapReduce to process the big data. The concept of cache
was applied in the system to provide immediate response and timely
action using big data analytics. Cache enabled big data increases the
response time of the system. The unstructured data provided by the
users are efficiently handled through map reduce algorithm. The
processing of complaints takes place in the order of the hierarchy of
the authority. The drawbacks of the traditional database system used
in the existing system are set forth by our system by using Cache
enabled Hadoop Distributed File System. MapReduce framework
codes have the possible to leak the sensitive data through
computation process. We propose a system that add noise to the
output of the reduce phase to avoid signaling the presence of
sensitive data. If the complaints are not processed in the ample time,
then automatically it is forwarded to the higher authority. Hence it
ensures assurance in processing. A copy of the filed complaint is sent
as a digitally signed PDF document to the user mail id which serves
as a proof. The system report serves to be an essential data while
making important decisions based on legislation.
Abstract: This paper describes the problem of building secure
computational services for encrypted information in the Cloud
Computing without decrypting the encrypted data; therefore, it meets
the yearning of computational encryption algorithmic aspiration
model that could enhance the security of big data for privacy,
confidentiality, availability of the users. The cryptographic model
applied for the computational process of the encrypted data is the
Fully Homomorphic Encryption Scheme. We contribute a theoretical
presentations in a high-level computational processes that are based
on number theory and algebra that can easily be integrated and
leveraged in the Cloud computing with detail theoretic mathematical
concepts to the fully homomorphic encryption models. This
contribution enhances the full implementation of big data analytics
based cryptographic security algorithm.
Abstract: Over the past era, there have been a lot of efforts and
studies are carried out in growing proficient tools for performing
various tasks in big data. Recently big data have gotten a lot of
publicity for their good reasons. Due to the large and complex
collection of datasets it is difficult to process on traditional data
processing applications. This concern turns to be further mandatory
for producing various tools in big data. Moreover, the main aim of
big data analytics is to utilize the advanced analytic techniques
besides very huge, different datasets which contain diverse sizes from
terabytes to zettabytes and diverse types such as structured or
unstructured and batch or streaming. Big data is useful for data sets
where their size or type is away from the capability of traditional
relational databases for capturing, managing and processing the data
with low-latency. Thus the out coming challenges tend to the
occurrence of powerful big data tools. In this survey, a various
collection of big data tools are illustrated and also compared with the
salient features.