Mining Device-Specific Apps Usage Patterns from Appstore Big Data

2018 ◽  
pp. 55-76
Author(s):  
Huoran Li ◽  
Xuanzhe Liu ◽  
Hong Mei ◽  
Qiaozhu Mei
Keyword(s):  
Big Data ◽  
Usage Patterns

MATHEMATICAL ALGORITHMS OF GAME THEORY AS AN APPLIED TOOL FOR MAKING EFFECTIVE FINANCIAL AND ECONOMIC DECISIONS

2020 ◽  
Vol 1 (12) ◽  
pp. 45-62
Author(s):  
L.S. Zvyagin ◽  
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Technological Development ◽  
New Production ◽  
Russian Government ◽  
Economic Decisions ◽  
Communications Technologies ◽  
Production Concept ◽  
Usage Patterns ◽  
Mathematical Algorithms

The relevance of the topic is due to the fact that today the digital economy determines that the market leaders are determined not by a longterm success story, not by the value of real estate and assets, not by the number of patents or access to capital, but by the ability to change and adapt their business to new conditions. Digital technologies, which have emerged over the past decade, help to find sources of increased efficiency and opportunities for rapid competitive development of enterprises. At the same time, they demand to change the existing management models, reformat communications, technologies and the organizational structure of enterprises based on new values, priorities and guidelines based on partnership, customer focus, innovation and synergy. Today's global manufacturing landscape is changing rapidly. The current technological development and the development of big data allow managers to better understand their activities. Big data provides companies with huge opportunities to improve their performance. Industry 4.0 and the Internet make it possible to create intelligent factories where machines and networks are able to exchange and respond to information, as well as independently manage the production process. Recently, the Russian Government has been advocating a new production concept, namely redistributed production, which uses a number of new technologies, such as 3D printing, additive manufacturing, and big data, to provide numerous advantages over existing systems. As such, businesses will need to adapt to changing data usage patterns to operate effectively in the growing digital age.

Extracting Usage Patterns from Power Usage Data of Homes' Appliances in Smart Home using Big Data Platform

2016 ◽  
Vol 11 (2) ◽  
pp. 39-50 ◽  
Author(s):  
Ali Reza Honarvar ◽  
Ashkan Sami
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Home ◽  
Pattern Mining ◽  
Greenhouse Gas Emission ◽  
Smart Cities ◽  
Sequence Pattern ◽  
Sequence Of Events ◽  
Usage Patterns ◽  
Usage Data

Advances in sensing techniques and IOT enabled the possibility to gain precise information about devices in smart home and smart city environments. Data analysis for sensors and devices may help us develop friendlier systems for smart city or smart home. Sequence pattern mining extracts interesting sequence pattern from data. Electricity usage dose follow a sequence of events. In this study the authors investigate this issue and extracted valuable sequence pattern from real appliances' power usage dataset using PrefixSpan. The experiments in this research is implemented on Spark as a novel distributed and parallel big data processing platform on two different clusters and interesting findings are obtained. These findings show the importance of extracting sequence pattern from power usage data to various applications such as decreasing CO2 and greenhouse gas emission by decreasing the electricity usage. The findings also show the needs to bring big data platforms to processing such kind of data which is captured in smart home and smart cities.

Exploring Big Data Footprints and Ethics

2019 ◽  
Vol 10 (2) ◽  
pp. 1-15
Author(s):  
Virginia M. Miori ◽  
Richard T. Herschel
Keyword(s):  
Big Data ◽  
Digital Media ◽  
Undergraduate Students ◽  
Social Connectedness ◽  
Smart Devices ◽  
Online Activity ◽  
Usage Patterns ◽  
Streaming Services ◽  
Study Participants ◽  
Ethical Perspectives

Big Data is collected via engagement in online activity and undergraduate students tend to be particularly heavy users of digital media. This article explores their online activity to assess their participation and usage patterns as well as their ethical perspectives. The research finds that these students have a somewhat substantial Big Data footprint since they actively engage in social media, use smart devices, shop online, use streaming services, and employ digital tools. Social connectedness necessitates the potential for their privacy being compromised and the findings suggest that introverts are more concerned about this issue then extroverts are. However, people of both types are concerned about conveying a positive image online. The majority of those surveyed primarily identified with the values expressed by the Utilitarian and Kantian ethical perspectives and less so with those expressed by Social Contract Theory and Virtue Ethics. However, study participants did not consistently ground their moral values in any one of these ethical theories.

scholarly journals Dependency profiles as a tool for big data analysis of linguistic constructions: a case study of emoticons

2017 ◽  
Vol 8 (2) ◽  
pp. 127-153
Author(s):  
Veronika Laippala ◽  
Aki-Juhani Kyröläinen ◽  
Jenna Kanerva ◽  
Juhani Luotolahti ◽  
Filip Ginter
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Computer Mediated Communication ◽  
Big Data Analysis ◽  
Support Vector ◽  
Mediated Communication ◽  
Computer Mediated ◽  
Usage Patterns ◽  
Narrative Constructions

This study presents a methodological toolbox for big data analysis of linguistic constructions by introducing dependency profiles, i.e., co-occurrences of linguistic elements with syntax information. These were operationalized by reconstructing sentences as delexicalized syntactic biarcs, subtrees of dependency analyses. As a case study, we utilize these dependency profiles to explore usage patterns associated with emoticons, the graphic representations of facial expressions. These are said to be characteristic of Computer-Mediated Communication, but typically studied only in restricted corpora. To analyze the 3.7-billion token Finnish Internet Parsebank we use as data, we apply clustering and support vector machines. The results show that emoticons are associated with three typical usage patterns: stream of the writer’s consciousness, narrative constructions and elements guiding the interaction and expressing the writer’s reactions by means of interjections and discourse particles. Additionally, the more frequent emoticons, such as :), are used differently than the less frequent ones, such as ^_^.Kokkuvõte. Veronika Laippala, Aki-Juhani Kyröläinen, Jenna Kanerva, Juhani Luotolahti ja Filip Ginter: Sõltuvusprofiilid kui vahend suurandmete keeleliste konstruktsioonide analüüsimiseks: uurimus emotikonidest. Uurimuses esitame metodoloogilise “tööriistakomplekti” keelekonstruktsioonide analüüsimiseks suurandmete põhjal, rakendades sõltuvusprofiile. Sõltuvusprofiil on lingvistiliste elementide koosesinemise esitusviis, kuhu on kaasatud süntaktiline informatsioon. Selleks on laused konstrueeritud sõltuvusanalüüsi alampuudena, kus süntaktiline info on esitatud sõnadevaheliste (kaksik-)kaarte abil. Artiklis rakendame sõltuvusprofiile selleks, et selgitada välja emotikonide kasutusmustrid. Näomiimika graafilised esitused on iseloomulikud arvuti suhtlusele, mida tavaliselt uuritakse piiratud korpuse põhjal, kuid meie kasutame klasterdamist ja tugivektor-masinaid 3,7 miljardi sõna suuruse Soome Interneti Puudepangal. Selgub, et emotikonide kasutus seostub kolme peamise kasutusmustriga: kirjutaja teadvuse vooluga, narratiivsete konstruktsioonidega ning hüüdsõnade ja diskursusepartiklitega, mis juhivad suhtlust ja väljendavad kirjutaja reaktsioone. Lisaks selgub, et sagedastel emotikonidel nagu :), on rohkem erinevaid kasutusi kui harvadel emotikonidel nagu ^_^.Võtmesõnad: sõltuvusprofiilid; kasutuspõhine süntaks; arvutisuhtlus; emotikonid; veebikorpus; soome keel

High Performance Query Execution and Graph Visualization for Effectual Big Data Analytics with Cloud

2020 ◽  
Vol 17 (6) ◽  
pp. 2713-2715
Author(s):  
Prachi Garg ◽  
Sandip Kumar Goel ◽  
Sakshi Sachdeva ◽  
Neelam Oberoi
Keyword(s):  
Big Data ◽  
High Performance ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Streaming Data ◽  
Graph Visualization ◽  
Multiple Sources ◽  
Need To Evaluate ◽  
Usage Patterns

The domain of data science contains enormous approaches and high performance techniques in which there is need to evaluate the data from multiple dimensions so that the effectual outcomes and predictive knowledge can be extracted. Data Science and Analytics is now days one of the conspicuous streams of advanced knowledge discovery. Following are the key constituents and assorted elements which are required in the data science for cavernous and multi-dimensional analytics of the datasets including Streaming of Data from Multiple Sources and Channels, Pre-Processing and Cleaning of Real Time Streaming Data, Feature Engineering and Extraction of Prime Elements from Datasets, Numerical Analysis and Scientific Computations, Statistical Analytics on Datasets, Data Engineering Visualization, Plotting and Predictive Analytics. The paper is presenting the usage patterns and cavernous analytics of big data with the high performance visualization using Grafana.

scholarly journals CLOUD-BASED DATA ANALYTICS FRAMEWORK FOR MOBILE APP EVENT ANALYSIS

2017 ◽  
Vol 10 (13) ◽  
pp. 207
Author(s):  
Pranav Vilas Vaidya ◽  
Janaki Meena M ◽  
Syed Ibrahim Sp
Keyword(s):  
Big Data ◽  
Mobile Applications ◽  
Mobile Application ◽  
Mobile App ◽  
Business Strategies ◽  
Event Analysis ◽  
Holistic View ◽  
Service Information ◽  
Usage Patterns ◽  
Efficient Data

Mobile analytics studies the behavior of end users of mobile applications and the mobile application itself. These mobile applications, being an important part of the various businesses products, need to be monitored and the usage patterns are to be analyzed. The data collected from these apps can help to drive important business strategies by identifying the usage patterns. Enriching the data with information available from other sources, like sales/service information, provides holistic view about the solution. Thus, here we aim at exploring some set of tools that give capabilities as event trailing with higher extraction of its linguistics. If the application is used worldwide, the data generated out of it is Big Data, which traditional systems cannot handle. We therefore propose a special framework for efficient data collection, storage and processing at Big Data scale on cloud platform.  

Improve Home Energy Management System by Extracting Usage Patterns From Power Usage Big Data of Homes' Appliances

2018 ◽  
pp. 126-141 ◽  
Author(s):  
Ali Reza Honarvar ◽  
Ashkan Sami
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Energy Management ◽  
Smart Cities ◽  
Processing System ◽  
Peak Time ◽  
Valuable Insight ◽  
Home Energy Management ◽  
Usage Patterns ◽  
Home Energy Management System

Many researchers have focused on the reduction of electricity usage in residences because it is a significant contributor of CO2 and greenhouse gases emissions. However, electricity conservation is a tedious task for residential users due to the lack of detailed electricity usage. Home energy management systems (HEMS) are schedulers that schedule and shift demands to improve the energy consumption on behalf of a consumer based on demand response. In this chapter, valuable sequence patterns from real appliances' usage datasets are extracted in peak time and off-peak time of weekdays and weekends to get valuable insight that is applicable in the HEMS. Generated data in smart cities and smart homes are placed in the category of big data. Therefore, to extract valuable information from such data an architecture for the home and city data processing system is proposed, which considers the multi-source smart cities and homes' data and big data processing platforms.

scholarly journals Dynamics of hotel website browsing activity: the power of informatics and data analytics

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Irene Cheng Chu Chan ◽  
Jing Ma ◽  
Rob Law ◽  
Dimitrios Buhalis ◽  
Richard Hatter
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Temporal Dynamics ◽  
Big Data Analytics ◽  
Strategic Partnership ◽  
Customer Engagement ◽  
Content Type ◽  
Usage Patterns ◽  
Post Hoc ◽  
Luxury Hotel

PurposeThis paper aims to investigate the temporal dynamics of users browsing activity on a hotel website in order to derive effective marketing strategies and constantly improve website effectiveness. Users' activities on the hotel's website on yearly, monthly, daily and hourly basis are examined and compared, demonstrating the power of informatics and data analytics.Design/methodology/approachA total of 29,976 hourly Weblog files from 1 August 2014 to 31 December 2017 were collected from a luxury hotel in Hong Kong. ANOVA and post-hoc comparisons were used to analyse the data.FindingsUsers' browsing behaviours, particularly stickiness, on the hotel website differ on yearly, monthly, daily and weekly bases. Users' activities increased steadily from 2014 to 2016, but dropped in 2017. Users are most active from July to September, on weekdays, and from noon to evening time. The month-, day-, and hour-based behaviours changed through years. The analysis of big data determines strategic and operational management and marketing decision-making.Research limitations/implicationsUnderstanding the usage patterns of their websites allow organisations to make a range of strategic, marketing, pricing and distribution decisions to optimise their performance. Fluctuation of website usage and level of customer engagement have implications on customer support and services, as well as strategic partnership decisions.Originality/valueLeveraging the power of big data analytics, this paper adds to the existing literature by performing a comprehensive analysis on the temporal dynamics of users' online browsing behaviours.

Job qualifications study for data science and big data professions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwah Ahmed Halwani ◽  
S. Yasaman Amirkiaee ◽  
Nicholas Evangelopoulos ◽  
Victor Prybutok
Keyword(s):  
Big Data ◽  
Data Science ◽  
Semantic Analysis ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Job Descriptions ◽  
Content Type ◽  
Professional Groups ◽  
Curriculum Guidelines ◽  
Usage Patterns

PurposeThe lack of clarity in defining data science is problematic in both academia and industry because the former has a need for clarity to establish curriculum guidelines in their work to prepare future professionals, and the latter has a need for information to establish clear job description guidelines to recruit professionals. This lack of clarity has resulted in job descriptions with significant overlap among different related professional groups. This study examines the industry view of five professions: statistical analysts (SAs), big data analytics professionals (BDAs), data scientists (DSs), data analysts (DAs) and business analytics professionals (BAs). The study compares the five fields with the unified backdrop of their common semantic dimensions and examines their recent dynamics.Design/methodology/approach1,200 job descriptions for the five Big Data professions (SA, DS, BDA, DA and BA) were pulled from the Monster website at four points in time, and a document library was created. The collected job qualification records were analyzed using the text analytic method of Latent Semantic Analysis (LSAs), which extract topics based on observed text usage patterns.FindingsThe findings indicated a good alignment between the industry view and the academic view of data science as a blend of statistical and programming skills. This industry view remained relatively stable during the 4 years of our study period.Originality/valueThis research paper builds upon a long tradition of related studies and commentaries. Rather than relying on subjective expertise, this study examined the job market and used text analytics to discern a space of skill and qualification dimensions from job announcements related to five big data professions.

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