Big Data as a Driving Tool of Digital Transformation

Adusumalli, Harshini Priya

doi:10.18034/abcjar.v5i2.616

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…Several other categorization experiments were been out using Regression as well. The methodologies and predictors of volatility forecasting, as well as the classification criteria, were identified in the work (Adusumalli, 2016b). Characteristics that were employed as indicators of literary volatility parameters and forecasting methodologies that were used to improve the precision of literature volatility parameters and prevision needs have been identified.…”

Section: Review and Survey Articlesmentioning

confidence: 99%

The Application of Machine Learning Techniques in Software Project Management- An Examination

Pasupuleti¹

2018

ABC j. adv. res.

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Planning and evaluating project management are key parts of project performance that should not be overlooked. It is difficult to succeed at project management unless you have a realistic and logical plan in place. This paper provides a comprehensive overview of papers on the application of machine learning in software project management, covering a wide range of topics. Apart from that, this study examines machine learning, software project management, and methodologies. Papers in the first category are the results of software project management studies or surveys. Papers in the third category are based on machine-learning methods and strategies applied to projects; studies on the phases and tests that are the parameters used in machine-learning management; and final classes of study results, contribution of studies to production, and promotion of machine-learning project prediction. Our work also provides a larger perspective and context, which could be useful for future project risk management research, among other things. To summarize, we have demonstrated that project risk assessment using machine learning is more effective in minimizing project losses, increasing the likelihood of project success, providing an alternative method for efficiently reducing project failure probabilities, increasing the output ratio for growth, and facilitating accuracy-based analysis of software fault prediction.

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Section: Review and Survey Articlesmentioning

confidence: 99%

The Application of Machine Learning Techniques in Software Project Management- An Examination

Pasupuleti¹

2018

ABC j. adv. res.

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“…To be more specific, the imminent arrival of massive amounts of information necessitates the development of tools in the fields of Big Data and Data Analytics in order to explore and evaluate this information (Adusumalli, 2016b). In this context, a number of writers have worked on a variety of applications that attempt to extract value from the raw data collected by smart meters or AMI assets.…”

Section: Introductionmentioning

confidence: 99%

Advanced Metering Infrastructure Data: Overviews for the Big Data Framework

Rahman

Pasupuleti²,

Adusumalli³

2019

ABC res. alert

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The Advanced Metering Infrastructure (AMI) statistics provide real-time information about power use as well as social, demographic, and economic aspects within a community. This study proposes a Data Analytics/Big Data architecture for leveraging AMI data in Smart City applications. The framework has three main components. First, the architectural view positions AMI within the SGAM. Second, the methodological view describes the DIKW hierarchy and NIST Big Data interoperability model's translation of raw data into knowledge. Finally, human expertise and talents to analyze the results and translate knowledge into wisdom are a connecting aspect between the two approaches. A binding element that supports optimal and efficient decision-making is added to our new perspective. We made a case study to demonstrate our framework. A load forecasting application is implemented in Retail Electricity Provider (REP). Some of the REP's marketplaces have a MAPE of less than 5%. The scenario also highlights the binding element's effect on new development options and as a feedback mechanism for more forceful decision making.

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“…The internet of things can meet these needs (CoT). CoT provides the platform for IoT device integration (Adusumalli, 2016b). Volume 7, No 1/2018 | AJASE This is achieved by locating and constructing the most appropriate virtual objects (Dehury and Sahoo, 2016).…”

Section: Introductionmentioning

confidence: 99%

Cloud of Things and Interworking IoT Platform: Strategy and Execution Overviews

Fadziso

Adusumalli

Pasupuleti³

2018

Asian j. appl. sci. eng.

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An IoT platform is a multi-layer system that enables linked device automation. IoT platforms are enabling software that connects various hardware devices, access points, and networks to other sections of the value chain. Virtual objects are now essential in any IoT platform. In this work, we design and create a cloud-based IoT platform that allows users to register and initialize virtual objects, then consume them via the IoT marketplace and integrate them into IoT applications. The proposed IoT platform differs from previous IoT platforms in that it provides both hardware and software services on a single platform. The proposed IoT platform is distinct from the IoT marketplace where users can buy and sell virtual things. Based on virtual items in CoT, IoT platform and IoT marketplace experiments are undertaken. The proposed IoT platform is easy to use, secure, and trustworthy. An IoT testbed and a case study for reusing virtual objects in a residential environment are developed. It allows for virtual object discovery and sharing. Virtual objects can monitor and operate IoT devices.

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Big Data as a Driving Tool of Digital Transformation

Cited by 9 publications

References 5 publications

The Application of Machine Learning Techniques in Software Project Management- An Examination

The Application of Machine Learning Techniques in Software Project Management- An Examination

Advanced Metering Infrastructure Data: Overviews for the Big Data Framework

Cloud of Things and Interworking IoT Platform: Strategy and Execution Overviews

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