Application of Big Data

This paper examines the transformative role of technology, particularly artificial intelligence (AI), in fostering active citizen participation and engagement in public administration within Massachusetts. By leveraging AI and digital tools, public agencies have enhanced service delivery, transparency, and inclusivity, enabling more effective interactions between governments and citizens. The research highlights key initiatives, including Boston's AI-powered 311 system, Cambridge's Open Data Program, and Worcester's Smart City Project, which collectively showcase how AI optimizes responsiveness, promotes transparency, and improves urban management. These case studies illustrate how AI-driven platforms provide real-time citizen feedback, personalized services, and predictive analytics to address public needs proactively. However, integrating AI in governance is not without challenges. Issues such as data privacy, algorithmic bias, the digital divide, and public trust pose significant barriers to equitable and ethical implementation. The research underscores the need for robust regulatory frameworks, digital inclusion initiatives, and ethical oversight to mitigate these challenges and ensure AI technologies serve all citizens effectively. Massachusetts's experience offers valuable insights into the potential and limitations of AI-driven public administration, emphasizing the importance of transparency, accessibility, and citizen-centric governance in building an engaged and empowered populace in the digital age.

There is always a need to modernize energy delivery of traditional power grids, using intelligent devices and big data technologies, this make them smart. The modernization is performed by deploying equipment such as sensors, smart meters, and communication devices, and by invoking procedures such as real-time data processing and big data analysis. A large data is generated by Smart grids all the time which needs to be analysed. This paper presents definitions and background of smart grid and big data. There are many challenges faced in collecting and analysing this data. In this paper current studies and research developments of big data applications in smart grid are discussed. This paper also summarizes the application leveraged by big data technologies, challenges and opportunities are pointed out in this

The explosive growing number of data from mobile devices, social media, Internet of Things and other applications has highlighted the emergence of big data. This paper aims to determine the worldwide research trends on the field of big data and its most relevant research areas. A bibliometric approach was performed to analyse a total of 6572 papers including 28 highly cited papers and only papers that were published in the Web of Science TM Core Collection database from 1980 to 19 March 2015 were selected. The results were refined by all relevant Web of Science categories to computer science, and then the bibliometric information for all the papers was obtained. Microsoft Excel version 2013 was used for analyzing the general concentration, dispersion and movement of the pool of data from the papers. The t test and ANOVA were used to prove the hypothesis statistically and characterize the relationship among the variables. A comprehensive analysis of the publication trends is provided by document type and language, year of publication, contribution of countries, analysis of journals, analysis of research areas, analysis of web of science categories, analysis of authors, analysis of author keyword and keyword plus. In addition, the novelty of this study is that it provides a formula from multi-regression analysis for citation analysis based on the number of authors, number of pages and number of references.

The National Payments Corporation of India (NPCI) has invested a sizable amount of money in the country's massive payment infrastructure in an effort to enhance the user experience. However, in order for investments to be profitable, NPCI must guarantee the ongoing use of technological solutions and post-adoptive behaviors like continuance and recommendation intention. The impact of cognitive factors (i.e. Performance expectancy, effort expectancy, social influences, facilitating conditions; personal innovativeness) and affective factors (such as satisfaction) on conative factors (such as continuation and recommendation intention) in the perspective of UPI applications (apps) was investigated using the UTAUT model. Partial Least Square Structural Equation Modeling when applied on 651 users (PLS-SEM) showed that satisfaction had a direct impact on continuation intentions, which in turn had an impact on recommendations intentions. It was discovered that all cognitive factors, including performance expectations, effort expectations, and facilitating conditions, have an impact on satisfaction. According to the study, adding a significant individual difference variable-personal innovativeness with regard to information technology-would aid in our understanding of the role that these factors play in the development of continuous intention. It further examines the influence of trust and security, and the pace of innovation on continued intentions. Through the mediating function of user satisfaction, it also looked at the impact of performance expectancy, effort expectancy, social influence, facilitating variable, and personal innovativeness on the continuance intentions of the UPI system. All factors have been shown to be significant. Future researchers will find it extremely helpful that the study used a validated instrument to better understand user adherence and referral intentions. Therefore, this study adds to the limited body of knowledge in the payment industry literature by examining how users perceive UPI apps and post-adoption behaviors.

Managing big data in cloud environments requires a combination of strong security measures and efficient
clustering techniques. With the increasing volume of sensitive data being processed, ensuring secure
storage, retrieval, and management is essential. Advanced cryptographic methods can protect data from
unauthorized access, while machine learning algorithms improve clustering accuracy and efficiency. By
integrating these technologies, cloud infrastructures can enhance both security and performance, reducing
vulnerabilities and optimizing resource allocation. Businesses and organizations relying on cloud platforms
need robust mechanisms to safeguard their data while maintaining operational efficiency. Efficient
clustering techniques help in organizing and processing large datasets, making data management more
effective. Secure cloud based operations ensure compliance with regulatory standards and protect against
cyber threats. Implementing scalable security frameworks strengthens data confidentiality, integrity, and
availability. Leveraging AI-driven models for anomaly detection further enhances security within cloud
environments. The proposed system will integrate security and clustering by using encrypted data storage
combined with machine learning-based data clustering within a cloud environment.

The landscape of consumer behavior in insurance markets has evolved significantly in recent years, driven predominantly by advancements in big data analytics. This paper examines the dynamics inherent in bundled insurance offerings, emphasizing the role of data-driven insights in shaping consumer preferences and decision-making processes. By analyzing extensive datasets from various channels-such as online interactions, customer feedback, and transaction records-this study delineates the critical factors that influence consumer choices when selecting bundled insurance products. Through a multifaceted analytical approach, the research reveals patterns and correlations that were previously obscured, ultimately providing a comprehensive understanding of consumer tendencies. The findings indicate that consumers are increasingly drawn to personalized bundled offerings that align with their unique needs and risk profiles. Such preferences are shaped by their experience with digital interfaces, pricing strategies, and the perceived value of the bundles. By leveraging machine learning algorithms and predictive modeling techniques, insurers can segment their customer base more effectively, leading to tailored marketing strategies that resonate with specific demographics. The application of big data analytics not only enhances operational efficiency but also fosters deeper customer engagement by enabling insurers to anticipate and respond to evolving consumer demands. In conclusion, this paper contributes to the field by illustrating how big data analytics can transform the approach to consumer behavior analysis in the insurance sector, especially regarding bundled products. The integration of these insights into business strategies allows insurers to maximize their competitive edge, fostering innovation while meeting the nuanced needs of contemporary consumers. As the industry continues to adapt to technological advancements, the recommendations provided outline a pathway for insurance firms aiming to enhance customer satisfaction and loyalty through informed, data-driven decision-making processes.

This chapter is dedicated to the memory of Sue Atkins, the Grande Dame of lexicography, who passed away in 2021. In a prologue we argue that she must be seen on a par with other visionaries and their visions, such as Paul Dirac in mathematics or Ludwig van Beethoven in music. We proceed to review the last half century, through the eyes of Sue Atkins. In the process, the insights of other luminaries come into the picture, including those of Patrick Hanks, Michael Rundell, Adam Kilgarriff, John Sinclair, and Charles Fillmore. This material is used as a background to start thinking out of the box in terms of the future of dictionaries. In all, about 50 oppositions are presented, in which the past is contrasted with the future, divided into five subsections: (1) the dictionary-making process, (2) supporting tools and concepts, (3) the appearance of the dictionary, (4) facts about the dictionary, and (5) the image of the dictionary. Moving from the future of dictionaries to the future of lexicographers, the argument is made that we need to join forces with the Big Data companies, which, by its nature, brings us to America and thus Americans, including Gregory Grefenstette, Erin McKean, Laurence Urdang, and Sidney Landau. In an epilogue, the presentation’s methodology is defined as being ‘a fact-based extrapolation of the future’, and includes good advice from Steve Jobs.

The research explores & elaborates on the application & influence of Big Data on the main areas or functions of various financial institutions, mainly commercial banks. Big Data has become an integral part of modern finance & economy. This study aggregates the main factors associated with the application of Big Data over various financial activities. The research also explores the complexities & constraints of Big Data within the lens of financial activities. The research follows a secondary data collection technique by systematically locating & reviewing academic research focusing on Big Data and finance or banking activities. Ultimately, the study provides a comprehensive overview of the fundamental characteristics of Big Data, its applications in financial institutions, related complexities & potential strategies to minimize the complexities.

- The recent trend in big data application has led the organizations to think about its application. Having in mind the importance of supply chain integration and learning the current study has attempted to explore the drivers of firm performance from the supply chain perspective. Data were collected from the employees working in the pharmaceuticals companies in Indonesia. Smart-PLS was used for data analysis. The results of the study showed that there is strong significant positive relationship between big data analytical capability and firm performance. Further the results also revealed a significant influence of supply chain integration and learning on the firm performance. The results also revealed supply chain agility as a significant positive mediator between the relationships of supply chain integration, big data analytical capability, learning and firm performance. All the hypothesis were accepted.

The storage of historical information consists of the registration of large concentrations of data based on samples of information issued by a given system. Historical databases play a key role in industrial control systems that process and provide the information needed for the tasks of supervision, event records, management, and maintenance in the industrial sector. The purpose of the present research is to analyze the current state of monitoring and control systems, recognizing their advantages and disadvantages, and reaching the state of the art about the selection of a NoSQL database for the recording of time series. The development of the work focused on the comparison of columnoriented storage managers as HBase, Cassandra, and Big Table . After designing and implementing two cases of tests for comparing the behavior of both managers in the scenarios of Intensive Readings and Reading/Writing, it was concluded that both of them present excellent performance for storing historic records.

Sensors, satellites, mobile devices, social media, e-commerce, and the Internet, among others, saturate us with data. The Internet of Things, in particular, enables massive amounts of data to be generated more quickly. The Internet of Things is a term that describes the process of connecting computers, smart devices, and other data-generating equipment to a network and transmitting data. As a result, data is produced and updated on a regular basis to reflect changes in all areas and activities. As a consequence of this exponential growth of data, a new term and idea known as big data have been coined. Big data is required to illuminate the relationships between things, forecast future trends, and provide more information to decision-makers. The major problem at present, however, is how to effectively collect and evaluate massive amounts of diverse and complicated data. In some sectors or applications, machine learning models are the most frequently utilized methods for interpreting ...

The term “Big Data” refers to a large volume of information usually in terabytes and
petabytes. It includes both structured and unstructured data. Unstructured data is
conventionally text-heavy, but may also contain data such as facts, dates, and numbers.
To use this unstructured information effectively, it needs to be processed and organized.
Taxonomy is considered a powerful way of organizing information. For automatic
taxonomy generation, various techniques have been proposed in the past. However, the
substantial nature of big data presently crosses the processing abilities of traditional
techniques. Thus, to meet this challenge an extensible and scalable technique is required
to potentially accelerate the process of taxonomy generation and its evolution upon
arrival of new data, hence catering to a large amount of unstructured big data. This paper
proposes a technique for both the taxonomy generation and evolution of Apache Spark
infrastructure. The proposed technique is evaluated on a text dataset from a computing
domain. The evaluation results show that the technique presented in this paper
outperformed the existing techniques in terms of time and quality metrics. The time
and quality-based evaluation showed that the use of the MapReduce environment has
resolved the scalability issues of the current taxonomy generation and evolution process.

Big data mainly refers to a huge volume of rapidly
growing data over size exabytes (1018). A major chunk of this
data is unstructured text data produced from several sources.
In order to use such data effectively, they need to be processed
and organized. Taxonomy, a hierarchical structure, is
considered an effective way of organizing the data. In the past,
many techniques have been proposed to generate taxonomy
automatically. Recently some attempts have also been made to
evolve the static structure of taxonomy to deal with the rapidly
changing nature of data. However, the voluminous nature of
today’s data currently exceeds the processing capabilities of
conventional techniques. In this regard, there is a need for a
scalable technique that potentially speeds up the process of
taxonomy generation and evolution and caters to a large
amount of unstructured big data. This paper presents a
technique for both the generation and the evolution of
taxonomy on the Apache Spark framework. The technique is
tested on a text dataset belonging to a computing domain. The
test results show that the scalable taxonomy generation and
evolution technique proposed in this paper is not only timeefficient
but also produces a good quality taxonomy as
compared to state-of-the-art techniques.

The healthcare industry is undergoing a significant transformation in data
management, spurred by the integration of artificial intelligence (AI) and cloud
technologies in data warehousing. This paper investigates the transformative
potential of AI-driven Extract, Transform, Load (ETL) processes and cloud
integration within healthcare data warehouses. We explore how these
technologies address key challenges such as data integration, real-time
processing, and scalability, which are critical in healthcare environments. By
examining various applications and proposing an implementation framework,
this study provides a roadmap for optimizing healthcare data warehouses to
support enhanced patient care, operational efficiency, and advanced analytics
capabilities.

he healthcare sector is experiencing an explosion in the volume, variety and velocity of data being generated. Leveraging
Artificial Intelligence (AI) to automate the data warehousing process offers transformative potential for managing this vast
array of patient data. AI-powered automation can facilitate real-time data analysis, improve decision-making, optimize resource
allocation and enhance overall patient care. This article explores the integration of AI in healthcare data warehousing, focusing on
its capacity to enhance automation, real-time data processing and data quality. It provides a comprehensive review of the current
state of data warehousing technologies in healthcare and outlines a roadmap for advancing data-driven healthcare systems.

The insurance and risk management sectors are experiencing a significant transformation driven by
the need for more sophisticated data analysis and predictive modeling. This paper explores the critical role of
artificial intelligence (AI) in enhancing Extract, Transform, Load (ETL) processes for modernizing data
warehouses within these industries. We examine how AI-enhanced ETL addresses key challenges such as data
quality, integration of diverse data sources, and real-time processing. The study investigates various applications
and proposes a framework for successful implementation. Our findings suggest that AI-driven ETL offers
unprecedented opportunities for operational efficiency, improved risk assessment, and competitive advantage in
insurance and risk management.

In the financial sector, the scale and complexity of data have increased exponentially, pushing legacy data
warehousing solutions to their limits. Cloud-based architectures, when combined with artificial intelligence (AI), provide
financial institutions with future-proof, scalable data management solutions capable of processing massive datasets for real
time insights and predictive analytics. This paper examines how AI and cloud technologies are revolutionizing financial data
warehouses, enabling institutions to scale their data assets and create future-proof ecosystems. We explore the synergies
between AI, cloud computing, and advanced data warehousing techniques and how they are reshaping the landscape of
financial data management and analysis. This article highlights the benefits of AI-driven ETL processes, real-time analytics,
and scalable storage, while examining the security, compliance, and operational efficiencies achieved in a cloud-based financial
ecosystem.

The integration of Artificial Intelligence (AI) and Big Data is revolutionizing the financial sector by transforming
operations, enhancing decision-making, and enabling predictive insights. This paper explores the profound
impact of these technologies on financial services, emphasizing applications in risk management, fraud detection,
customer experience, and regulatory compliance. It also addresses challenges such as data privacy, algorithmic
bias, and integration with legacy systems. By incorporating additional insights from recent advancements and
related domains, the article offers a comprehensive roadmap for financial institutions to harness the
transformative power of AI and Big Data while mitigating associated risks

The energy and utilities sector is experiencing significant transformation driven by the
integration of cloud-based data warehousing and artificial intelligence (AI). This paper
explores the potential of these technologies in tackling major industry challenges, including
infrastructure modernization, energy consumption optimization, market volatility, and the
transition to sustainable energy solutions. Cloud-based data warehousing provides a scalable
platform for managing large quantities of operational and consumption data, while AI
leverages this data to deliver actionable insights. Key applications covered in this study include
predictive maintenance, demand forecasting, grid optimization, enhanced customer
experience, and energy consumption analysis. Additionally, the challenges of implementing
these technologies—such as data security, regulatory compliance, and workforce adaptation—
are addressed. The findings suggest that cloud-based data warehousing and AI present
unprecedented opportunities for operational efficiency, innovation, asset management, and
sustainability in the energy and utilities sector. This research contributes to the evolving body
of knowledge on digital transformation in critical infrastructure industries and outlines a
roadmap for future developments.

The financial sector is undergoing a profound transformation through the integration of artificial intelligence (AI)
and big data into traditional data warehousing frameworks, facilitating advanced predictive analytics. This paper
examines the role of AI, big data, and Enterprise Application Integration (EAI) in enhancing financial data
warehousing, enabling institutions to overcome challenges related to data integration, real-time processing, and
predictive modeling. We analyze key applications in risk management, fraud detection, and algorithmic trading,
highlighting the role of EAI in bridging legacy systems with advanced analytics. Our findings suggest that
integrating these technologies enables financial institutions to achieve operational efficiency, innovation, and
competitive advantage.

The financial sector is undergoing a profound transformation through the integration of artificial intelligence (AI)
and big data into traditional data warehousing frameworks, facilitating advanced predictive analytics. This paper
examines the role of AI, big data, and Enterprise Application Integration (EAI) in enhancing financial data
warehousing, enabling institutions to overcome challenges related to data integration, real-time processing, and
predictive modeling. We analyze key applications in risk management, fraud detection, and algorithmic trading,
highlighting the role of EAI in bridging legacy systems with advanced analytics. Our findings suggest that
integrating these technologies enables financial institutions to achieve operational efficiency, innovation, and
competitive advantage.

Query answers from on-line databases can easily be corrupted by hackers or malicious intent by the database publisher. Thus it is important to provide mechanisms which allow clients to trust the results from on-line queries. Authentic publication is a novel scheme which allows untrusted publishers to securely answer queries from clients on behalf of trusted o-line data owners. Publishers validate

XML is increasingly becoming the format of choice for information exchange, in critical areas such as government, finance, healthcare and law, where integrity is of the essence. As this trend grows, one can expect that documents (or collections thereof) may get quite large, and clients may wish to query for specific segments of these documents. In critical applications, clients must be assured that they are getting complete and correct answers to their queries. Existing methods for signing XML documents cannot be used to establish that an answer to a query is complete. A simple approach has a server processing queries and certifying answers by digitally signing them with an on-line private key; however, the server, and its on-line private key, would be vulnerable to external hacking and insider attacks. We propose a new approach to signing XML documents which allows untrusted servers to answer certain types of path queries and selection queries over XML documents without the need for trusted on-line signing keys. This approach enhances both the security and scalability of publishing information in XML format over the internet. In addition, it provides greater flexibility in authenticating parts of XML documents, in response to commercial or security policy considerations.

Transformacije različitih aspekata vrednovanja
kulturnog nasleđa odvijaju se paralelno sa tehnološkim
promenama poslednjeg veka, pri čemu umrežavanje igra ključnu
ulogu u evolutivnom procesu. Proučavanje iskustava i analiza percepcije korisnika, te potreba za njihovim merenjem i kvantifikovanjem, predstavljaju ključni aspekt vremena u kome živimo, a on je oblikovan modelima komunikacije i interpretacije baštine koji su povezani s informa-
tičkim tehnologijama. Kako navodi Loradžejn Smit (Smith, 2010), pravo nasleđe, kada su naše emocije i osećaj sopstva istinski angažovani, nije toliko u posedovanju materijalnog koliko je to u činu prenošenja i primanja sećanja i znanja. Oslanjajući se na to, rad rasvetljava pitanje odnosa između onoga što je zaštićeno kao kulturno-istorijska vrednost,
koja je pod kriterijumima Uneskove Konvencije o svetskoj
baštini prepoznata kao univerzalna, i načina na koji to
prepoznaju i doživljavaju sami posetioci sledećih lokaliteta na Kosovu i Metohiji
1) manastir Visoki Dečani;
2) manastir Pećka patrijaršija;
3) manastir Gračanica;
4) crkva Bogorodica Ljeviška.
Istraživanje sprovedeno analizom podataka – sadržaja 3.772 utiska korisnika predstavljena putem platforme Gugl mape na izabranim lokacijama – daje odgovor na pitanje da li posetioci lokaliteta
vide iste izazove i lepote kao i predstavnici institucija
i na koji način nam tako prikupljena saznanja mogu pomoći
u očuvanju kulturne baštine.

In the age of big data, machine learning (ML) is increasingly critical for extracting insights from large datasets. The rise of cloud infrastructure has transformed data processing, offering scalable resources and cost-effective solutions for machine learning tasks. This paper explores the methods and challenges associated with preparing data for machine learning in a cloud environment. Key processes include data collection, cleaning, transformation, and integration, all essential for optimizing ML models. Challenges such as data privacy, security, and latency are also addressed. The paper further reviews the advantages of using cloud platforms for ML, including scalability, flexibility, and ease of collaboration. Despite these benefits, there remain significant challenges, particularly concerning data transfer, cost management, and ensuring the quality of data preparation. This study provides a comprehensive overview of current methodologies and identifies research gaps that suggest avenues for future exploration. By leveraging cloud infrastructure effectively, organizations can enhance their ML capabilities, resulting in more accurate predictions and better decision-making.

The rise of real-time mobile applications has transformed how users interact with technology, demanding instantaneous data processing and seamless user experiences. Real-time applications have become integral across industries, including social media, gaming, healthcare, and financial services. This paper explores various strategies for developing real-time mobile applications, emphasizing the technological advancements and architectural considerations that enable real-time functionality. It begins by defining realtime applications and their importance, followed by an examination of critical technologies such as WebSockets, server-sent events, and push notifications. The paper also addresses challenges in real-time app development, such as latency, scalability, and data synchronization, offering solutions like edge computing, efficient data handling, and optimized network protocols. Furthermore, it evaluates popular frameworks and tools, including Firebase, Pusher, and AWS AppSync, which facilitate the development of real-time features. Security and privacy concerns are also considered, highlighting encryption, authentication, and data protection measures. Through case studies and industry examples, the paper illustrates the successful implementation of real-time capabilities and the benefits they deliver to users and businesses. By providing a comprehensive guide to the strategies and technologies involved in developing real-time mobile applications, this paper aims to equip developers and businesses with the knowledge to create responsive and dynamic applications that meet modern user expectations.

In the evolving landscape of financial services, the monetization of financial data analytics has emerged as a pivotal strategy for gaining competitive advantage and driving business growth. As financial institutions increasingly harness vast amounts of data, leveraging analytics to generate actionable insights and derive financial value has become essential. This paper explores best practices for monetizing financial data analytics, focusing on how organizations can effectively transform data into profitable assets.

Dengan perubahan pelabuhan yang modern banyak dipengaruhi oleh berbagai faktor yang terjadi seperti ekonomi global, kebijakan nasional, lingkungan pelabuhan, rantai pasokan, teknologi baru dan pembangunan secara berkelanjutan. Lebih dari 80% data tidak digunakan secara efisien sehingga pemangku kepentingan di seluruh ekosistem pelabuhan sekarang bekerja sama dengan cara baru yang didorong oleh data[1]. Peningkatan efisiensi pelabuhan dalam kegiatan operasional maka perlu adanya teknologi yang menghasilkan data dalam jumlah besar, yang datang dalam berbagai bentuk dan harus dibagikan dengan banyak pemangku kepentingan untuk mendukung dalam pengambilan keputusan atau biasa di sebut Decision Support System (DSS)[2]. Teknologi Big Data pada pelabuhan dapat memberikan solusi terhadap tantangan internal dan eksternal yang unik dari organisasi secara efisien dengan memberikan pelayanan informasi data secara real timesehingga dengan mudah menjalankan operasional secara efektif serta memprediksi situasi pelabuhan di masa depan. Penelitian ini merupakan studi literatur yang fokus pada menjelaskan arsitektur dan bisnis proses serta teknologi Big Data yang digunakan. Hasil penelitian ini akan menunjukkan bahwa penerapan teknologi Big Data terkait dengan berkembangnya digitalisasi akan menjadi suatu acuan di dalam informasi pelayanan pelabuhan di Indonesia.

The exportation of goods and services plays an essential part in the growth of an economy, exposing producers of goods and services to an external market(s) that fosters collaboration within and outside nations of the world. Some states rely more on exporting goods than others in the United States, and different products are exported at higher rates between states. The specialization of exports can benefit a state since those products help its economy succeed. Understanding what exported goods are essential to the economy allows people to decide what should be produced and exported. Kansas has its own set of goods that it exports across the world. By observing their exporting habits, policymakers in Kansas will know where they stand and how Original Research Article

Purpose:  The main purpose of examining innovation development (ID), technology management (TM) and big data analytics capability (BDAC) from the perspective of information technology companies is to help these companies optimize their business strategies and increase their competitiveness. When these concepts are considered together, it is aimed to present suggestions that information technology companies can increase their innovation capacities, optimize their technology portfolios and develop their big data analytics capacities.

Design/methodology/approach: Data were collected from information technology companies working on big data analytics in technoparks in Istanbul. In the research, the Marmara region of Turkey was preferred because it is the region where the information technology sector is most common. In total, 503 questionnaires were collected. SmartPLS (4.0.8.4) licensed software was used in the research, and the results are presented with tables and figures.

Findings: As a result of the analysis of the data, it is supported by hypotheses that ID and TM have positive effects as independent variables and BDAC has positive effects as both independent and mediation variables.

Research limitations/implications: In terms of the limitations of the research, since the data were collected only from the information technology companies in the technoparks in Istanbul, it would not be correct to generalize the analysis results. For this reason, it is recommended to develop a research model and contribute to the literature by considering this limited situation for similar studies to be conducted in the future.

Practical implications: By focusing on ID, it is important for companies to analyze their innovation processes and increase their ID capacity. On the subject of TM, analyses help companies identify their current technological infrastructure and development needs and optimize their technology portfolios. Big data analytics is an important tool that companies can use in their decision-making processes. Therefore, analyses of big data analytics can evaluate companies' current data analytics capacities and offer improvement suggestions.

Originality/value: So why are ID, TM and BDAC important? Why should a research model be developed to examine the effects of these variables? This situation can be understood by looking at the investments made by two world-class companies with headquarters in Istanbul/Turkey. L'Oréal Turkey integrates big data, cloud computing, artificial intelligence and digital platforms into its business processes by investing in new technologies and also makes a difference with innovation in environmental sustainability and social responsibility. PepsiCo, on the other hand, placed a great emphasis on innovation by opening its third Design and Innovation Center in Turkey and Europe in Istanbul. For this reason, examining the effects of ID, TM and big data analytics together in the research is important for the originality of the research. Examining these variables by focusing on their interactions and effects increases the originality of the subject.

Auspiciously, big data analytics had made it possible to generate value from immense amounts of raw data. Organizations are able to seek incredible insights which assist them in effective decision making and providing quality of service by establishing innovative strategies to recognize, examine and address the customers’ preferences. However, organizations are reluctant to adopt big data solutions due to several barriers such as data storage and transfer, scalability, data quality, data complexity, timeliness, security, privacy, trust, data ownership, and transparency. Despite the discussion on big data opportunities, in this paper, we present the findings of our in-depth review process that was focused on identifying as well as analyzing the transient and permanent barriers for adopting big data. Although, the transient barriers for big data can be eliminated in the near future with the advent of innovative technical contributions, however, it is challenging to eliminate the perma...

The electrical industry is dominated by AC supply medium for more than a century now. In last few decades, the environmental pollution and limited resources for non-renewable energy led us to shift towards more sustainable renewable energy resources and smart efficient appliance for electricity saving. In modern electricity system Hybrid grid is much more energy efficient and reliable technology. In Hybrid system has both types of AC and DC supply and appliance are included with proper power modulator. In this paper optimized a hybrid grid with different types of DGs and AC-DC loads in linear platform using MILP algorithm with taken the example of a home as well as compared the electricity bill with previous condition. Here we done the work for automation of home as well as optimized hybrid grid and saw many benefits over the conventional grid.

The study aims to observe the dynamics of unemployment rate and the number of job openings in Java as the region that becomes the main crutch of the Indonesian economy and analyze the relationship between supply and demand of labor in during the pandemic using Web Scraping and Pearson correlation analysis. Web scraping results show that the largest number of job openings in Java Island early 2021 are in West Java and DKI Jakarta and the most demanded jobs are sales, marketing, followed by administration staff, low-level accounting, and finance. The correlation analysis shows a weak positive relationship of 0.45 between labor market supply and demand using the keyword" Lowongan Kerja" based on the google trends search index and a moderate positive of 0.54 using the keyword "Loker" on the google trends search index.

The analysis of large volumes of data is an important activity in manufacturing companies, since they allow improving the decision-making process. The data analysis has generated that the services and products are personalized, and how the consumption of the products has evolved, obtaining results that add value to the companies in real time. In this case study, developed in a large manufacturing company of electronic components as robots and AC motors; a strategy has been proposed to analyze large volumes of data and be able to analyze them to support the decision-making process; among the proposed activities of the strategy are: Analysis of the technological architecture, selection of the business processes to be analyzed, installation and configuration of Hadoop software, ETL activities, and data analysis and visualization of the results. With the proposed strategy, the data of nine production factors of the motor PCI boards were analyzed, which had a greater incidence in the rej...

Özetçe-Bu çalışmada, lise ögrencilerinin egitimlerinde yardımcı olması için tasarlanan Türkçe soru cevaplama sisteminin soru analizi kısmında kullanılmak üzere geliştirilen kural tabanlı yöntem sunulmuştur. Soru içerisinden, cevabın türü ve niteligini belirten kelimelerin (odak) elde edilmesinde kullanılan bu yöntem, uzmanlar tarafından elle toplanıp işaretlenmiş cografya soruları kümesi üzerinde deneye tabi tutulmuş, sonuçları belirtilmiştir. Kurallar cografya alanına baglı kalınmayıp, genel Türkçe soru kalıpları üzerine kuruldugu için bu yöntem, alandan bagımsız olarak herhangi bir Türkçe soru cevaplama sisteminde kullanılabilir. Hem kaynak kodları, hem de hazırlanan soru kümesi, çalışmanın tekrarlanabilmesi ve daha da geliştirilebilmesi adına elektronik olarak saglanmıştır. Anahtar Kelimeler-soru cevaplama sistemleri, soru analizi, kural tabanlı bilgi özütleme, odak Abstract-This study describes a rule-based approach that is employed in the question analysis module of the Turkish question answering system we design for high-school students to assist their education. The technique is used to extract parts of the question (the focus) that indicate the type and the character of the answer. Evaluation is performed on a set of Geography question data that are collected and annontated by human experts, and the results are provided. Manually tailored rules are based on general Turkish question patterns, thereby not dependent on the domain. Therefore, this technique can be used by any Turkish question answering system, regardless of the domain. Both the source codes and the annontated data set are electronically provided for both reproducability and future work.

Osmanlıca, yüzyılları kapsayan bir tarihe ışık tutabilecek, onlarca neslin yaşantılarını, hayallerini, bilgi birikimini kapsayan zengin bir dildir. Ancak Arap alfabesini temel alan karmaşık yapısı ve Türkçe’nin ihtiyaçlarını karşılamakta zorlanması nedeni ile modern çağa uyum sağlayamamış ve değişime uğramıştır. Evrişimsel Sinir Ağları Tabanlı Osmanlıca Belge Çözümleyici projesi kapsamında, tarihi belgeler üzerinde araştırma yapmak isteyen insanların önüne çıkan yeni bir alfabe öğrenme zorluğunu gidermek ve Osmanlıca yazılmış belgeleri anlamalarını kolaylaştırmak için bir platform geliştirilmesi amaçlanmıştır. Platform, kullanıcının görüntüsünü verdiği Osmanlıca belgenin içinde kullanıcının çevirmek istediği metni seçmesini ve bu metnin perspektif dönüşüm ile düzeltilerek sonraki görüntü işleme adımlarına hazır hale gelmesini sağlayan bir araç bulundurmaktadır. Seçilen metin otomatik görüntü işleme yöntemleri ile satırlarına, kelimelerine ve karakterlerine ayrıldıktan sonra bir Evri...

This paper traces convergence and innovation processes in five local newspapers in the Inland Norway region. It is an explorative bottomup study of how local legacy newspapers' use of digital technology relates to organisational factors and business models, as well as their journalism. Based on interviews with chief editors and video journalists, the paper shows that these local newspapers adapted in specific ways, sometimes differ from their national counterparts. Each newspaper established a web-edition and later implemented a paywall. This change of business model relates to a change of focus from clicks to subscribers. The change is also reflected in their journalism, focussing on certain topics and more in-depth reporting. While attempts at video reporting turned out to be expensive, streaming local sports became an important element in attracting new subscribers. However, it was easier for those newspapers integrated in larger conglomerates to use these innovations as the parent company provided competence in analysing viewer figures, and because innovations could spread from one local newspaper in the company to the others.

Currently, the process of improving techniques ensuring a certain level of security of internet data transfer is underway. The procedures for secure processing and sharing of information are improved. The continuation of these processes is the improvement of security systems for data transfer on the Internet carried out by permanently updating and adapting the risk management process of IT systems to the constantly changing ICT and Industry 4.0 technologies. For the purposes of the subject discussed in this study, the authors presented the results of research in the field of sentiment analytics in the Big Data database system. In the field of sentiment analysis of issues regarding the security of data transfer on the Internet, the term ransomware was chosen. Research on sentiment analysis of the ransomware term was conducted from June 2017 to February 2019.

The success of using workflows for modeling large-scale scientific applications has fostered the research on parallel execution of scientific workflows in shared-nothing clusters, in which large volumes of scientific data may be stored and processed in parallel using ordinary machines. However, most of the current scientific workflow management systems do not handle the memory and data locality appropriately. Apache Spark deals with these issues by chaining activities that should be executed in a specific node, among other optimizations such as the in-memory storage of intermediate data in RDDs (Resilient Distributed Datasets). However, to take advantage of the RDDs, Spark requires existing workflows to be described using its own API, which forces the activities to be reimplemented in Python, Java, Scala or R, and this demands a big effort from the workflow programmers. In this paper, we propose a parallel scientific workflow engine called TARDIS, whose objective is to run existing workflows inside a Spark cluster, using RDDs and smart caching, in a completely transparent way for the user, i.e., without needing to reimplement the workflows in the Spark API. We evaluated our system through experiments and compared its performance with Swift/K. The results show that TARDIS performs better (up to 138% improvement) than Swift/K for parallel scientific workflow execution.

Duygusal Twitter, kullanıcıya kullanım kolaylığı sağlayan ve görsel kullanıcı ara yüzü ile hem bireysel, hem de kurumsal kullanıcılar için Twitter verisini ayrıştıran, analiz eden ve raporlayan bir programdır. Duygusal Twitter'da her tweet için olumlu, olumsuz ve nötr olmak üzere 3 farklı sonuç döndürülmektedir. Çalışmada hem sözlük hem de n-gram modeli kullanılarak iki yöntem geliştirilmiştir. Sözlük yöntemi, n-gram yöntemine göre daha başarılı sonuçlar vermiştir. Sentimental Twitter software is parsing, analyzing and reporting Twitter data, giving service to individuals and corporate users via its user friendly graphical user interface. Each tweet is classified as positive, negative or neutral in Sentimental Twitter. In this study, both lexicon and n-gram method has been used to perform and implement two different methods. As a result the lexicon method has been measured more performance than the n-gram method.

Duygusal Twitter, kullanıcıya kullanım kolaylığı sağlayan ve görsel kullanıcı ara yüzü ile hem bireysel, hem de kurumsal kullanıcılar için Twitter verisini ayrıştıran, analiz eden ve raporlayan bir programdır. Duygusal Twitter'da her tweet için olumlu, olumsuz ve nötr olmak üzere 3 farklı sonuç döndürülmektedir. Çalışmada hem sözlük hem de n-gram modeli kullanılarak iki yöntem geliştirilmiştir. Sözlük yöntemi, n-gram yöntemine göre daha başarılı sonuçlar vermiştir. Sentimental Twitter software is parsing, analyzing and reporting Twitter data, giving service to individuals and corporate users via its user friendly graphical user interface. Each tweet is classified as positive, negative or neutral in Sentimental Twitter. In this study, both lexicon and n-gram method has been used to perform and implement two different methods. As a result the lexicon method has been measured more performance than the n-gram method.

Sentimental Twitter software is parsing, analyzing and reporting Twitter data, giving service to individuals and corporate users via its user friendly graphical user interface. Each tweet is classified as positive, negative or neutral in Sentimental Twitter. In this study, both lexicon and n-gram method has been used to perform and implement two different methods. As a result the lexicon method has been measured more performance than the n-gram method.