scholarly journals 5C, A New Model of Defining Big Data

2017 ◽  
Vol 4 (1) ◽  
pp. 48-61 ◽  
Author(s):  
Liang-Jie Zhang ◽  
◽  
Jing Zeng ◽  
◽  
◽  
...  
Keyword(s):  
Big Data ◽  
New Model

scholarly journals Recommender System using Content Based Filtering for News Portal in Indonesia

2020 ◽  
Vol 8 (6) ◽  
pp. 173-178
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Recommender System ◽  
Information Overload ◽  
Cosine Similarity ◽  
News Article ◽  
The Internet ◽  
New Model ◽  
Digital Era ◽  
Content Based Filtering

Nowadays there is much news on the internet. It makes the reader become information overload. The reader does not know the most important news for them. The digital era, especially in Indonesia, generated data in Bahasa very fast that referred to as big data. Data mining by process big data can collect the data insight that the reader already read. This paper proposes a new model to proceed with Bahasa news and use the TF-IDF method to collect the feature of the article. Cosine similarity from the news article used to rank the new unknown articles to recommend articles based on their preference. we can filtering the stream of information and highlight the most likely article they will read but based on their preference that we already collect implicitly from the article that they read it, it’s a scroll depth of the article they read.Then we can serve the news more personalized from what they love to read.

scholarly journals 5C, A New Model of Defining Big Data

2015 ◽  
Vol 2 (4) ◽  
pp. 10-23 ◽  
Author(s):  
Liangjie Zhang ◽  
◽  
Jing Zeng ◽  
◽  
◽  
...  
Keyword(s):  
Big Data ◽  
New Model

scholarly journals A new model for identifying emerging technologies

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Stephanie F. Hughes
Keyword(s):  
Big Data ◽  
Emerging Technologies ◽  
Intelligence Community ◽  
Competitive Advantages ◽  
New Model ◽  
Emerging Trends ◽  
The Status ◽  
Search Speed ◽  
The Many ◽  
The Impact

Today, the complexity of so many emerging technologies requires anunderstanding of adjacent technologies often originating from multiple industries. Technology sequence analysis has been used by organizations, governments and industries to help make sense of the many variables impacting the evolution of technologies. This technique relies heavily on the input of experts who can offer perspectives on the status of current technologieswhile also highlighting the potential opportunities in the future. However, the volume and speed at which scientific research is accelerating is making it nearly impossible for even the most knowledgeable expert to stay current with research in their own industries. Today however, the use of big data search tools can help identify emerging trends around disruptive technologieswell before many of the experts have fully grasped the impact of these technologies. Despite the fear of many in the intelligence community that these tools will make their jobs obsolete, we expect that the value of the intelligence expert will increase given their unique knowledge of relevant data sources and how to connect the data in meaningful ways to derive value for the firm. We propose a new forecasting model that incorporates a combination of technologysequencing analysis and big data tools within the organization while also leveraging experts from across the open innovation spectrum. This new model, informed by current client engagements, has the potential to create significant competitive advantages for organizations as they benefit from expanded search breadth, search depth and search speed all while leveraging a range of internal and external experts to make sense of the rapidly changingtechnological landscape confronting their environment.

Adoption of Big Data analytics in construction: development of a conceptual model

2019 ◽  
Vol 9 (4) ◽  
pp. 564-579 ◽  
Author(s):  
Jiwat Ram ◽  
Numan Khan Afridi ◽  
Khawar Ahmed Khan
Keyword(s):  
Big Data ◽  
Conceptual Model ◽  
Big Data Analytics ◽  
Environmental Benefits ◽  
Relative Advantage ◽  
Adoption Process ◽  
Senior Managers ◽  
Content Type ◽  
New Model ◽  
Improved Design

PurposeBig Data (BD) is being increasingly used in a variety of industries including construction. Yet, little research exists that has examined the factors which drive BD adoption in construction. The purpose of this paper is to address this gap in knowledge.Design/methodology/approachData collected from literature (55 articles) were analyzed using content analysis techniques. Taking a two-pronged approach, first study presents a systematic perspective of literature on BD in construction. Then underpinned by technology–organization–environment theory and supplemented by literature, a conceptual model of five antecedent factors of BD adoption for use in construction is proposed.FindingsThe results show that BD adoption in construction is driven by a number of factors: first, technological: augmented BD–BIM integration and BD relative advantage; second, organizational: improved design and execution efficiencies, and improved project management capabilities; and third, environmental: augmented availability of BD-related technology for construction. Hypothetical relationships involving these factors are then developed and presented through a new model of BD adoption in construction.Research limitations/implicationsThe study proposes a number of adoption factors and then builds a new conceptual model advancing theories on technologies adoption in construction.Practical implicationsFindings will help managers (e.g. chief information officers, IT/IS managers, business and senior managers) to understand the factors that drive adoption of BD in construction and plan their own BD adoption. Results will help policy makers in developing policy guidelines to create sustainable environment for the adoption of BD for enhanced economic, social and environmental benefits.Originality/valueThis paper develops a new model of BD adoption in construction and proposes some new factors of adoption process.

scholarly journals A New Model for Integrating Big Data into Phases of Decision-Making Process

2019 ◽  
Vol 151 ◽  
pp. 636-642 ◽  
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Making Process ◽  
New Model

scholarly journals Study of WAMS Big Data Elastic Store Model in Low-Frequency Oscillation Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hua Song ◽  
Yongjun Chen
Keyword(s):  
Big Data ◽  
Performance Test ◽  
High Reliability ◽  
Low Frequency ◽  
Wide Area ◽  
Forced Oscillations ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
New Model ◽  
Oscillation Analysis

Low-frequency oscillation (LFO) is among the key factors that threaten interconnected power grids’ security and stability and restrict transfer capability. In particular, power systems incur now and then weak damping and forced oscillations. To monitor and control LFO, the principles of online calculation and analysis of two types of LFO are studied in this paper. The big data of wide area measurements is an important information source of LFO analysis. Hence, we should make sure it has access to online system continuously, accurately, and reliably. Nevertheless, the conventional linear data store model has difficulty to meet the processing requirements of high rate, multiple concurrency, and high reliability. To deal with it, a new model of double-set elastic store is proposed in this paper. It transforms the storage space linear model to plane model, realizes the management of power system substation group sets in vertical direction and the management of multiple Phase Measurement Units (PMU) uploading data sets in horizontal direction, and hence solves the problems in continuous and reliable access of the wide area measurements data, which is dense and of large scale and has quick update rate, providing technical support of accuracy and robustness of LFO analysis. The performance test and practical application of the proposed new model of double-set elastic store validate its accuracy.

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