All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Becoming an analytics-driven organization helps companies reduce costs, increase
revenues and improve competitiveness, and this is why business intelligence and
analytics continue to be a top priority for CIOs. Many business decisions, however,
are still not based on analytics, and CIOs are looking for ways to reduce time to value
for deploying business intelligence solutions so that they can expand the use of
analytics to a larger audience of users.
Companies are also interested in leveraging the value of information in so-called big
data systems that handle data ranging from high-volume event data to social media
textual data. This information is largely untapped by existing business intelligence
systems, but organizations are beginning to recognize the value of extending the
business intelligence and data warehousing environment to integrate, manage, govern
and analyze this information.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Becoming an analytics-driven organization helps companies reduce costs, increase
revenues and improve competitiveness, and this is why business intelligence and
analytics continue to be a top priority for CIOs. Many business decisions, however,
are still not based on analytics, and CIOs are looking for ways to reduce time to value
for deploying business intelligence solutions so that they can expand the use of
analytics to a larger audience of users.
Companies are also interested in leveraging the value of information in so-called big
data systems that handle data ranging from high-volume event data to social media
textual data. This information is largely untapped by existing business intelligence
systems, but organizations are beginning to recognize the value of extending the
business intelligence and data warehousing environment to integrate, manage, govern
and analyze this information.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
Evolution of Data Analytics: the past, the present and the futureVarun Nemmani
This paper delves into the topic of advanced analytics, the current industry demands to utilize and analyze huge/diverse amounts of data, how big data analytics is becoming a part of the decision making process and to anticipate trends. This paper takes the reader from Analytics era 1.0 to the current Analytics era 3.0; shows the future projections of big data analytics and also the current leaders of the Big Data Analytics market.
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...SAP Analytics
Winning new business and satisfying customers are top agenda items in bank boardrooms worldwide. Executives are bullish on new technologies to meet these objectives.
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
Camssguide Big Data Analytics Solutions, can help you meet and exceed challenges and opportunities for business, industry, and technology solution areas
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfKajal Digital
Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
Evolution of Data Analytics: the past, the present and the futureVarun Nemmani
This paper delves into the topic of advanced analytics, the current industry demands to utilize and analyze huge/diverse amounts of data, how big data analytics is becoming a part of the decision making process and to anticipate trends. This paper takes the reader from Analytics era 1.0 to the current Analytics era 3.0; shows the future projections of big data analytics and also the current leaders of the Big Data Analytics market.
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...SAP Analytics
Winning new business and satisfying customers are top agenda items in bank boardrooms worldwide. Executives are bullish on new technologies to meet these objectives.
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
Camssguide Big Data Analytics Solutions, can help you meet and exceed challenges and opportunities for business, industry, and technology solution areas
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfKajal Digital
Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
Want Free Career Counseling?
Just fill in your details, and one of our experts will call you!
Call us: +918308103366
WhatsApp Us: https://wa.me/+918308103366
Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
Big data refers to the vast amount of structured and unstructured data that inundates organizations on a daily basis. This data comes from various sources such as social media, sensors, digital transactions, mobile devices, and more.
Data science is the practice of extracting, analyzing, and interpreting large amounts of data to identify trends, correlations, and patterns. It combines machine learning, statistics, programming, and data engineering tools to uncover insights that can inform business decisions. Data scientists collect, organize, and analyze large amounts of data to find valuable insights and make predictions. Data science can be used in various industries, from finance and health care to retail and advertising. By leveraging data-driven decision-making, companies are able to gain a better understanding of their customers, identify new growth opportunities, and optimize their operations.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Similar to Big Data Analytics: Challenges And Applications For Text, Audio, Video, And Social Media Data (20)
Design and Implementation of Smart Cooking Based on Amazon EchoIJSCAI Journal
Smart cooking based on Amazon Echo uses the internet of things and cloud computing to assist in cooking
food. People may speak to Amazon Echo during the cooking in order to get the information and situation of
the cooking. Amazon Echo recognizes what people say, then transfers the information to the cloud services,
and speaks to people the results that cloud services make by querying the embedded cooking knowledge and
achieving the information of intelligent kitchen devices online. An intelligent food thermometer and its mobile
application are well-designed and implemented to monitor the temperature of cooking food
Forecasting Macroeconomical Indices with Machine Learning : Impartial Analysi...IJSCAI Journal
The importance of economic freedom has often been stressed by supporters of liberalism, but can its actual
effect be observed in a data driven, objective way? To analyze this relation the Economic Freedom of the
World (EFW) index and the Human Development Index (HDI) were examined with modern machine learning algorithms and a wide-ranging approach. Considering the EFW index’s preference of a liberalistic
oriented economic policy, an objective recommendation for creating an economic policy that improves
people’s everyday lives might be derived by the analysis results. It was found that these more advanced
algorithms achieve a considerably stronger correlation between both indices than pure statistical means
yet leave a small room for interpretation towards a counter-liberalistic implementation of demand-driven
economic policy.
Intelligent Electrical Multi Outlets Controlled and Activated by a Data Minin...IJSCAI Journal
In the proposed paper are discussed results of an industry project concerning energy management in
building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a
logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural
network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets
placed into a building and having defined priorities. The priority rules are grouped into two level: the first
level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm,
together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting
management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three
for three outlets and able to control load matching with defined thresholds. The goal of the paper is to
provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the
electrical network in a building. Finally in the paper are analyzed the correlation between global active
power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction
and the correlation analyses provide information about load balancing, possible electrical faults and energy
cost optimization.
Nov 2018 Table of contents; current issue -International Journal on Soft Comp...IJSCAI Journal
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
6th international conference on artificial intelligence and applications (aia...IJSCAI Journal
6th International Conference on Artificial Intelligence and Applications (AIAP-2019) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Generating images from a text description is as challenging as it is interesting. The Adversarial network
performs in a competitive fashion where the networks are the rivalry of each other. With the introduction of
Generative Adversarial Network, lots of development is happening in the field of Computer Vision. With
generative adversarial networks as the baseline model, studied Stack GAN consisting of two-stage GANS
step-by-step in this paper that could be easily understood. This paper presents visual comparative study of
other models attempting to generate image conditioned on the text description. One sentence can be related
to many images. And to achieve this multi-modal characteristic, conditioning augmentation is also
performed. The performance of Stack-GAN is better in generating images from captions due to its unique
architecture. As it consists of two GANS instead of one, it first draws a rough sketch and then corrects the
defects yielding a high-resolution image.
Temporally Extended Actions For Reinforcement Learning Based Schedulers IJSCAI Journal
Temporally extended actions have been proved to enhance the performance of reinforcement learning
agents. The broader framework of ‘Options’ gives us a flexible way of representing such extended course of
action in Markov decision processes. In this work we try to adapt options framework to model an operating
system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting
for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and
subsequent context switch leads to considerable overhead. In this work we try to utilize the historical
performances of a scheduler and try to reduce the number of redundant context switches. We propose a
machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better
performing timeslice. We measure the importance of states, in option framework, by evaluating the impact
of their absence and propose an algorithm to identify such checkpoint states. We present empirical
evaluation of our approach in a maze-world navigation and their implications on "adaptive timeslice
parameter" show efficient throughput time.
Knowledgebase Systems in Neuro Science - A StudyIJSCAI Journal
The improvement of health and nutritional status of the society has been one of the thrust areas for social
developments programmes of the country. The present states of healthcare facilities in India are inadequate
when compared to international standards. The average Indian spending on healthcare is much below the
global average spending. Indian healthcare Industry is growing at the rapid pace of more than 18%, the
fastest in the world. The prospects for Indian healthcare are to the tune of USD 40 billion, while global
market is USD 1660 trillion. India has all the prospects to become medical tourism destination of the
world, because it has a large pool of low-cost scientifically trained technical personal and is one of the
favoured counties for cost effective healthcare. As per the reports of Global Burden of Neurological
Disorders Estimations and Projections survey there is big shortage of neurologist in India and around the
world. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to
gain expertise in Neuro Science and treat patients like expert neurologist. This paper aims to survey the
Soft Computing techniques in treating neural patient’s problems used throughout the world
An Iranian Cash Recognition Assistance System For Visually Impaireds IJSCAI Journal
In economical societies of today, using cash is an inseparable aspect of human’s life. People use cash for
marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and
the necessity of knowing the monetary value of it caused one of the most challenging problems for visually
impaired people. In this paper we propose a mobile phone based approach to identify monetary value of a
picture taken from a banknote using some image processing and machine vision techniques. While the
developed approach is very fast, it can recognize the value of the banknote by an average accuracy rate of
about 97% and can overcome different challenges like rotation, scaling, collision, illumination changes,
perspective, and some others.
An Experimental Study of Feature Extraction Techniques in Opinion MiningIJSCAI Journal
The feature selection or extraction is the most important task in Opinion mining and Sentimental Analysis
(OSMA) for calculating the polarity score. These scores are used to determine the positive, negative, and
neutral polarity about the product, user reviews, user comments, and etc., in social media for the purpose
of decision making and Business Intelligence to individuals or organizations. In this paper, we have
performed an experimental study for different feature extraction or selection techniques available for
opinion mining task. This experimental study is carried out in four stages. First, the data collection process
has been done from readily available sources. Second, the pre-processing techniques are applied
automatically using the tools to extract the terms, POS (Parts-of-Speech). Third, different feature selection
or extraction techniques are applied over the content. Finally, the empirical study is carried out for
analyzing the sentiment polarity with different features
Monte-Carlo Tree Search For The "Mr Jack" Board Game IJSCAI Journal
Recently the use of the Monte-Carlo Tree Search algorithm, and in particular its most famous
implementation, the Upper Confidence Tree can be seen has a key moment for artificial intelligence in
games. This family of algorithms provides huge improvements in numerous games, such as Go, Havannah,
Hex or Amazon. In this paper we study the use of this algorithm on the game of Mr Jack and in particular
how to deal with a specific decision-making process.Mr Jack is a 2-player game, from the family of board
games. We will present the difficulties of designing an artificial intelligence for this kind of games, and we
show that Monte-Carlo Tree Search is robust enough to be competitive in this game with a smart approach.
Unsupervised learning models of invariant features in images: Recent developm...IJSCAI Journal
Object detection and recognition are important problems in computer vision and pattern recognition
domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on
computer based systems has proved to be a non-trivial task. In particular, despite significant research
efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition
systems in real time remain elusive. Here we present a survey of one particular approach that has proved
very promising for invariant feature recognition and which is a key initial stage of multi-stage network
architecture methods for the high level task of object recognition.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Estimation Of The Parameters Of Solar Cells From Current-Voltage Characterist...IJSCAI Journal
This paper presents a method for calculating the light generated current, the series resistance, shun
resistance and the two components of the reverse saturation current usually encountered in the double
diode representation of
the solar cell from the experimental values of the current
-
voltage characteristics
of the cell using genetic algorithm. The theory is able to regenerate the above mentioned parameters to
very good accuracy when applied to cell data that was generated from
pre
-
defined parameters. The
method is applied to various types of space quality solar cells and sub cells. All parameters except the
light generated current are seen to be nearly the same in the case of a cell whose characteristics under
illumination and i
n dark were analyzed. The light generated current is nearly equal to the short
-
circuit
current in all cases. The parameters obtained by this method and another method are nearly equal
wherever applicable. The parameters are also shown to represent the cur
rent
-
voltage characteristics
well
Implementation of Folksonomy Based Tag Cloud Model for Information Retrieval ...IJSCAI Journal
In the magnitude of internet one need to devote extra time to investigate an
ticipated resource, especially
when one need to search information from documents. For the higher range internet there is serious need
to demand the essentiality to discover the reserved resources. One of the solutions for information retrieval
from docume
nt repository is to attach tags to documents. Numerous online social bookmarking services
permit users to attach tags with resources which are eventually meta
-
data, frequently stated as folksonomy.
In current paper, authors implemented this model for infor
mation retrieval by utilizing these tags, after
retrieving by using delicious API and synthesize tag cloud in an Indian University to search and retrieve
information from document repository
Study of Distance Measurement Techniques in Context to Prediction Model of We...IJSCAI Journal
Internet is the boon in modern era as every organization uses it for dissemination of information and ecommerce
related applications. Sometimes people of organization feel delay while accessing internet in
spite of proper bandwidth. Prediction model of web caching and prefetching is an ideal solution of this
delay problem. Prediction model analysing history of internet user from server raw log files and determine
future sequence of web objects and placed all web objects to nearer to the user so access latency could be
reduced to some extent and problem of delay is to be solved. To determine sequence of future web objects,
it is necessary to determine proximity of one web object with other by identifying proper distance metric
technique related to web caching and prefetching. This paper studies different distance metric techniques
and concludes that bio informatics based distance metric techniques are ideal in context to Web Caching
and Web Prefetching
A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATAIJSCAI Journal
Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
Design of Dual Axis Solar Tracker System Based on Fuzzy Inference SystemsIJSCAI Journal
Electric power is a basic need in today’s life. Due to the extensive usage of power, there is a need to look
for an alternate clean energy source. Recently many researchers have focused on the solar energy as a
reliable alternative power source. Photovoltaic panels are used to collect sun radiation and convert it into
electrical energy. Most of the photovoltaic panels are deployed in a fixed position, they are inefficient as
they are fixed only at a specific angle. The efficiency of photovoltaic systems can be considerably increased
with an ability to change the panels angel according to the sun position. The main goal of such systems is
to make the sun radiation perpendicular to the photovoltaic panels as much as possible all the day times.
This paper presents a dual axis design for a fuzzy inference approach-based solar tracking system. The
system is modeled using Mamdani fuzzy logic model and the different combinations of ANFIS modeling.
Models are compared in terms of the correlation between the actual testing data output and their
corresponding forecasted output. The Mean Absolute Percent Error and Mean Percentage Error are used
to measure the models error size. In order to measure the effectiveness of the proposed models, we
compare the output power produced by a fixed photovoltaic panels with the output which would be
produced if the dual-axis panels are used. Results show that dual-axis solar tracker system will produce
22% more power than a fixed panels system.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Leading Change strategies and insights for effective change management pdf 1.pdf
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And Social Media Data
1. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
DOI :10.5121/ijscai.2016.5105 41
BIG DATA ANALYTICS: CHALLENGES AND
APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND
SOCIAL MEDIA DATA
Jai Prakash Verma
1
, Smita Agrawal
1
, Bankim Patel
2
and Atul Patel
3
1
CSE Department, Institute of Technology, Nirma University, Ahmedabad
2
SRIMCA, UKA Trasadia University, Surat
3
CMPICA, CHARUSAT University, Changa
ABSTRACT
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
KEYWORDS
Big Data, Big Data Analytics, Social Media Analytics, Content Based Analytics, Text Analytics, Audio
Analytics, Video Analytics.
1. INTRODUCTION
The term big data is used to describe the growth and the availability of huge amount of structured
and unstructured data. Big data which are beyond the ability of commonly used software tools to
create, manage, and process data within a suitable time. Big data is important because the more
data we collect the more accurate result we get and able to optimize business processes. The Big
data is very important for business and society purpose. The data came from everywhere like
sensors that used to gather climate information, available post or share data on the social media
sites, video movie audio etc. This collection of data is called ―BIG DATA‖.
Now a days this big data is used in multiple ways to grow business and to know the world [1,2,
15].
In most enterprise scenarios the data is too big or it moves too fast or it exceeds current
processing capacity. Big data has the potential to help companies improve operations and make
faster, more intelligent decisions. Big data usually includes data sets with sizes beyond the ability
of commonly used software tools to capture, curate, manage, and process data within a tolerable
elapsed time. Big data is a set of techniques and technologies that require new forms of
integration to uncover large hidden values from large datasets that are diverse, complex, and of a
massive scale. Wal-Mart handles more than 1 million customer transaction every hour. Facebook
handles 40 billion photos from its user base. Big data require some technology to efficiently
process large quantities of data. It use some technology like, data fusion and integration, genetic
algorithms, machine learning, and signal processing, simulation, natural language processing,
time series Analytics and visualization [12,13,16]
2. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
42
1.1. Characteristics of Big Data:-
Volume: Many factors contribute to the increase in data volume. Transaction-based data stored
through the years. Unstructured data streaming in from social media. Increasing amounts of
sensor and machine-to-machine data being collected. In the past, excessive data volume was a
storage issue. But with decreasing storage costs, other issues emerge, including how to determine
relevance within large data volumes and how to use analytics to create value from relevant data
[10, 12,13, 15,16].
Velocity: Data is streaming in at unprecedented speed and must be dealt with in a timely manner.
RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-
real time. Reacting quickly enough to deal with data generation speed is a challenge for most
organizations.
Variety: Data today comes in all types of formats. Structured, numeric data in traditional
databases. Information created from line-of-business applications. Unstructured text documents,
email, video, audio, stock ticker data and financial transactions. Managing, merging and
governing different varieties of data is something many organizations still grapple with.
Variability: In addition to the increasing velocities and varieties of data, data flows can be highly
inconsistent with periodic peaks. Daily, seasonal and event-triggered peak data loads can be
challenging to manage. Even more so with unstructured data involved.
Complexity: Today's data comes from multiple sources. And it is still an undertaking to link,
match, cleanse and transform data across systems. However, it is necessary to connect and
correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out
of control.
Value: It includes how we can use this big data for enhancing the business and living style. We
know that different types of business or social application generate different types of data. Still
identifying values form Big Data in their application areas is a big issue.
2. BIG DATA ANALYTICS
Big Data Anlytics refers to the process of collecting, organizing, analyzing large data sets to
discover different patterns and other useful information. Big data analytics is a set of
technologies and techniques that require new forms of integration to disclose large hidden values
from large datasets that are different from the usual ones, more complex, and of a large enormous
scale. It mainly focuses on solving new problems or old problems in better and effective ways
[12,13, 15, 16].
The main goal of the big data analytic is to help organization to make better business
decision,future prediction, analysis large numbers of transactions that done in organization and
update the form of data that organization is used. Example of big data Analytics are big online
business website like Flipkart, snapdeal uses Facebook or Gmail data to view the customer
information or behaviour. Analyzing big data allows analysts, researchers, and business users to
make better and faster decisions using data that was previously inaccessible or unusable. Using
advanced analytics techniques such as text analytics, machine learning, predictive analytics, data
mining, statistics, and natural language processing, businesses can analyze previously untapped
data sources independent or together with their existing enterprise data to gain new insights
resulting in significantly better and faster decisions. It helps us to uncover hidden patterns,
unknown correlations, market trends, customer preferences etc. It leads us to more effective
marketing, revenue opportunities, better customer service etc. Big Data can be analyzed through
3. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
43
predictive analytics, text analytics, statistical analytics and data mining[1,2,4]. Types of big data
analytics are: Prescriptive: - This type of analytics help to decide what actions should be taken. It
very valuable but not used largely. It focuses on answer specific question like, hospital
management, diagnosis of cancer patients, diabetes patients that determine where to focus
treatment. Predictive: - This type of analytics help to predict future or what might be happen. For
example some companies use predictive analytics to take decision for sales, marketing,
production, etc. Diagnostic: - In this type look at past and analyze the situation what happen in
past and why it happen. And how we can overcome this situation. For example weather
preadiction, customer behavioral analysis etc. Descriptive:-It describes what is happening
currently and prediction near future. For example market analysis, compatains behavioral
analysis etc.
By using appropriate analytics organization can increase sales, increase customer service, and can
improve operations. Predictive Analytics allow organizations to make better and faster decisions
[1, 2, 4, 10].
2.1. Predictive Analytics
Predictive Analytics is a method through which we can extract information from existing data
sets to predict future outcomes and trends and also determine patterns. It does not tell us what
will happen in future. It forecasts what might happen in future with acceptable level of reliability.
It also includes what if-then-else scenarios and risk assessment. Applications areas of Predictive
Analytics are [1, 2, 4, 10]:
CRM (Customer Relationship Management): Predictive analytics is useful in CRM in fields such
as marketing campaigns, sales, customer services etc. The focus is to put their efforts effectively
on analyzeing product in demand and predict customer’s buying habits .
Clinical Decision Support: Predictive Analytics helps us to determine that which patients are at
risk of developing certain conditions like diabetes, asthma, lifetime illness etc.
Collection Analytics: Predictive Analytics helps financial institutions for the allocation for
collecting resources by identifying most effective collection agencies, contact strategies etc. to
each customer.
Cross Sell: An Organization that offers multiple products, Predictive Analytics can help to
analyze customer’s spendings, their behavior etc. This can help to lead cross sales that means
selling additional products to current customers.
Customer Retention: As the number of competing services is increasing, businesses should
continuously focus on maintaining customer satisfaction, rewarding loyal customers and
minimize customer reduction. If Predictive Analytics is properly applied, it can lead to active
retention strategy by frequently examining customer’s usage, spending and behavior patterns.
Direct marketing: When marketing consumer products and services, there is the challenge of
keeping up with competing products and consumer behavior. Apart from identifying prospects,
predictive analytics can also help to identify the most effective combination of product versions,
marketing material, communication channels and timing that should be used to target a given
consumer.
Fraud detection: Fraud is a big problem for many businesses and can be of various types:
inaccurate credit applications, fraudulent transactions (both offline and online), identity thefts and
false insurance. These problems plague firms of all sizes in many industries. Some examples of
4. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
44
likely victims are credit card issuers, insurance companies, retail merchants, manufacturers,
business-to-business suppliers and even services providers. Predictive analysis can help to
identify high-risk fraud candidates in business or the public sector.
Portfolio, product or economy-level prediction: These types of problems can be addressed by
predictive analytics using time series techniques. They can also be addressed via machine
learning approaches which transform the original time series into a feature vector space, where
the learning algorithm finds patterns that have predictive power.
Risk management: When employing risk management techniques, the results are always to
predict and benefit from a future scenario. Predictive analysis helps organizations or business
enterprises to identify future risk, Natural Disaster and its effect. Risk management helps them to
take correct decision on correct time.
Underwriting: Many businesses have to account for risk exposure due to their different services
and determine the cost needed to cover the risk. For example, auto insurance providers need to
accurately determine the amount of premium to charge to cover each automobile and driver. For
a health insurance provider, predictive analytics can analyze a few years of past medical claims
data, as well as lab, pharmacy and other records where available, to predict how expensive an
enrollee is likely to be in the future. Predictive analytics can help underwrite these quantities by
predicting the chances of illness, default, bankruptcy, etc. Predictive analytics can streamline the
process of customer acquisition by predicting the future risk behaviour of a customer using
application level data.
2.2. Big Data Analytics usage in India
From predicting ticket confirmations of trains to checking for water supply leakages and even for
finding the perfect bride and groom, Big Data is being used in a number of creative ways in
India. Following are few uses of Big Data Analytics in india in last few years [3,9].
a) Win elections (exit poll).
b) Finding a perfect match.
c) Detecting water leakages.
d) Gaining insights into shopping behavior.
e) Ensuring proper water supply.
f) Improve India’s financial inclusion ratio.
g) Improve product development.
h) Predict ticket confirmations for trains.
3. SOCIAL MEDIA ANALYTICS
The Social Media analytics is collecting information or data form the social media websites,
blogs etc. and uses it in business purpose or decision making. Now a Days Social Media is the
best platform for understand the real-time customer choice or intentions and sentiments, using
social media business advertising, product marketing easily. EBay.com uses two data warehouses
at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer
recommendations, and merchandising. Inside eBay’s 90PB data warehouse. Amazon.com
handles millions of back-end operations every day, as well as queries from more than half a
million third-party sellers. The core technology that keeps Amazon running is Linux-based and
as of 2005 they had the world’s three largest Linux databases, with capacities of 7.8 TB, 18.5
TB, and 24.7 TB. Facebook handles 50 billion photos from its user base. As of August 2012,
Google was handling roughly 100 billion searches per month [8, 9, 14].
5. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
45
3.1. Application areas
a) Behavior Analytics
b) Location-based interaction Analytics
c) Recommender systems development
d) Link prediction
e) Customer interaction and Analytics & marketing
f) Media use
g) Security
h) Social studies
3.2. Challenges of social media analytics
a) Massive amounts of data require lots of storage space and processing power.
b) Shifting social media platforms.
c) Worldwide online accessibility provides more data in many languages.
d) Evolution of online language.
4. CONTENT BASE ANALYTICS
Content Base Analytics means whatever data that store in social media back-end site. For
example Facebook users store their data, photos, and videos on Facebook storage. For this
content they need big amount of storage but now a days number of users increasing rapidly so,
social networking sites like Facebook, twitter, WhatsApp need to increase their storage capacity
day by day and that’s the obstacle because they don’t know how much of storage capacity they
need to increase.
Content-based predictive analytics recommender systems mostly match features (tagged
keywords) among similar items and the user’s profile to make recommendations. When a user
purchases an item that has tagged features, items with features that match those of the original
item will be recommended. The more features match, the higher the probability the user will like
the recommendation. This degree of probability is called precision. [4,6,13] User-based tagging,
however, turns up other problems for a content-based filtering system (and collaborative
filtering) like:
a) Credibility: Not all customers tell the truth (especially online), and users who have only a
small rating history can skew the data. In addition, some vendors may give (or encourage
others to give) positive ratings to their own products while giving negative ratings to their
competitors’ products.
b) Scarcity: Not all items will be rated or will have enough ratings to produce useful data.
c) Inconsistency: Not all users use the same keywords to tag an item, even though the meaning
may be the same. Additionally, some attributes can be subjective. For example, one viewer
of a movie may consider it short while another says it’s too long.
4. 1. Precision with constant feedback
One way to improve the precision of the system’s recommendations is to ask customers for
feedback whenever possible. Collecting customer feedback can be done in many different ways,
through multiple channels. Some companies ask the customer to rate an item or service after
6. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
46
purchase. Other systems provide social-media-style links so customers can ―like‖ or ―dislike‖ a
product.
4.2. Measurement for effectiveness of system recommendations
The success of a system’s recommendations depends on how well it meets two criteria: precision
(think of it as a set of perfect matches — usually a small set) and recall (think of it as a set of
possible matches — usually a larger set). Issues in measurement for effectiveness:
a) Precision measures how accurate the system’s recommendation was. Precision is difficult to
measure because it can be subjective and hard to quantify.
b) Some recommendations may connect with the customer’s interests but the customer may still
not buy. The highest confidence that a recommendation is precise comes from clear evidence:
The customer buys the item. Alternatively, the system can explicitly ask the user to rate its
recommendations.
c) Recall measures the set of possible good recommendations your system comes up with.
Think of recall as an inventory of possible recommendations, but not all of them are perfect
recommendations. There is generally an inverse relationship to precision and recall. That is,
as recall goes up, precision goes down, and vice versa.
The ideal system would have both high precision and high recall. But realistically, the best
outcome is to strike a delicate balance between the two. Emphasizing precision or recall really
depends on the problem you’re trying to solve [4,6,13].
5. TEXT ANALYTICS
Most of all information or data is available in textual form in databases. From these contexts,
manual Analytics or effective extraction of important information are not possible. For that it is
relevant to provide some automatic tools for analyzing large textual data. Text analytics or text
mining refers process of deriving important information from text data. It will use to extract
meaningful data from the text. It use many ways like associations among entities, predictive
rules, patterns, concepts, events etc. based on rules. Text analytics widely use in government,
research, and business needs. Data simply tells you what people did but text analytics tell you
why. From unstructured or semi structured text data all information will retrieve. From all textual
data it will extract important information. After extracting information it will be categorized. And
from these categorized information we can take decision for business [5, 6].
5.1. Steps for Text Analytics system (Figure -1):
a) Text: In initial stage data is unstructured.
b) Text processing: All information will transfer in Semantic Syntactic text.
c) Text transformation: In it important text will extract for future use.
d) Feature selection: In it data is counted and display in Statistics format.
e) Data mining: All data is classified and clustered.
7. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
47
Figure 1. The steps for Text Analytics system
5.2. Text Analytics applications areas:
a) Security application: It will we monitoring and analyzing internet blogs, news, social sites
etc. for national security purpose. It will use full detect unethical thing on internet.
b) Marketing application: By analyzing text data we can identify which type of product
customer most like.
c) Analyzing open – ended survey responses: In survey research one company ask to customer
some question like, pros and cons about some products or asking for suggestion. For
analyzing these types of data, text analytics is require.
d) Automatic process on emails and messages: By using big data analytics we can filter huge
amount of emails based on some terms or words. It is also useful when you want to
automatically divert messages or mails to appropriate department or section.
5.3. Distinct Aspects of Text in Social Media:
a) Time Sensitivity: An important feature of the social media services is their real-time nature.
With the rapid growth of the content and communication styles, text is also changing. As the
time sensitivity of the textual data the people’s thoughts also changes from time to time.
b) Short Length: Successful processing of the short texts is essential for the text analytics
method. As the messages are short, it makes people more efficient with their participation in
social networking websites. Short messages are used in social media which consists of few
phrases or sentences.
c) Unstructured Phrases: An important difference between the text in social media and
traditional media is the difference in the quality of content. Different people posts different
things according to their knowledge, ideas, and thoughts. When composing a message also
many new abbreviations and acronyms are used for e.g. How r u? ―Gr8‖ are actually not
words but they are popular in social media.
5.4. Applying Text Analytics to Social Media:
a) Event Detection: It aims to monitor a data source and detect the occurrence of an event that is
to be captured within that source. These data sources includes images, videos, audios, text
documents.
b) Collaborative Question Answering: As social networking websites has emerged, the
collaborative question answering services have also emerged. It includes several expert
8. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
48
people to answer the questions posted by the people. A large number of questions and
answers are posted on the social networking websites.
c) Social Tagging: Tagging of the data has also increased to a great extent. For example when
any particular user is looking or searching for a recent event like ―Bihar Election‖ then the
system will return the results that are tagged as ―Bihar‖ or ‖Election‖.
Textual data in social media provides lots of information and also the user-generated content
provides diverse and unique information in forms of comments, posts and tags. [5,6]
6. AUDIO ANALYTICS
Audio analytics is the process of compressing data and packaging the data in to single format
called audio. Audio Analytics refers to the extraction of meaning and information from audio
signals for Analysis. There are two way to represent the audio Analytics is 1) Sound
Representation 2) Raw Sound Files. Audio file format is a format for store digital audio data on a
system. There are three main audio format: Uncompressed audio format, Lossless compressed
audio format, Lossy compressed audio format. [11]
5.1. Application Area of Audio Analytics:
The audio is the file format that used to transfer the data to one place to another. Audio analytics
is used to check whether given audio data is available in proper format or in similar format that
sender send. The Application of audio Analytics are many:
a) Surveillance application: Surveillance application is based on approach for systematic choice
of audio classes for detection of crimes done in society. A surveillance application is based
on audio Analytics framework is the only way to detect suspicious kind of activity. The
application is also used to send some important information to surveillance at some crisis
situation urgently.
b) Detection of Threats: The audio mechanism is used to indentify the thread that take place
between sender and receiver.
c) Tele-monitoring System: New technology have camera with the facilities to record the audio
also. Audio Analytics may provide effective detection of screams, breaking glass, gun sound,
explosions, calling for help sound etc. Combination of audio Analytics and video Analytics
in single monitoring system result as a good threat detection efficiency.
d) Mobile Networking System: The Mobile networking system is used to talk or transfer
information to one place to another place. Sometimes due to some network problem the audio
sound is not work properly at that time Audio Analytics is used to find the information that
not send properly due to some problems.
7. VIDEO ANALYTICS
Video is a major issue when considering big data. Videos and images contribute to 80 % of
unstructured data. Now a days, CCTV cameras are the one form of digital information and
surveillance. All these information is stored and processed for further use, but video contains lots
of information and is generally large in size. For example YouTube has innumerable videos
being uploaded every minute containing a massive information. Not all video are important and
viewed largely. This creates a situation where videos create a junk and hard-core contribution to
big data problems. Apart from videos, surveillance cameras generate a lot of information in
seconds. Even a small Digital camera capturing an image stores millions of pixel information in
mille seconds.
9. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
49
VIDEO Data Analytics dimensions - Volume: Size of video being more, takes the network as
well as the server, time for processing. Low bandwidth connections create traffic on network as
these videos deliver slowly. When stored on mass storage on secondary storage requires huge
amount of space and takes more time retrieving as well as processing. Variety: Videos consisting
of various format and variety such as HD videos, Blu-ray copies etc. Velocity: It is speed of data.
Now a days, Digital cameras process and capture videos at a very high quality and high speed.
Video editing makes it to grow in size as it contains other extra information about the videos.
Videos grow in size faster as they are simply nothing but collection of images.[7]
7.1. Application of video analytics:
a) Useful in accident cases: With the use of CCTV cameras we can identify what happened at
the time of accident it’s also used for security reason and parking vehicles etc.
b) Useful in schools, traffic police, business, security etc.
c) Video Analytics for investigation (Video Search): Video analytics algorithms is implemented
to analyze video, a task that is challenging and its very time consuming for human operator
especially when there is large amount of data are available using video analytics we can
search particular video when we required.
d) Video analytics for Business Intelligence: It uses to extracts statistical and operational data.
Rather than having operator that review all the video and tally all the people or cars moving
in certain area, or checking which traffic routes are most commonly taken, video analytics
can do it automatically.
e) Target and Scene Analytics: Video Analytics for business Intelligence involves target and
scene Analytics. Target Analytics provides details information about the target movement,
patterns, appearance and other characteristics which can be used for identification of target.
f) Direction Analytics: Direction Analytics is the ability to distinguish behavior by assigning
specific values (low to high) to areas within a camera’s field of view.
g) Remove the human equation through the automation: It removes the tedium involved in
giving one or more set of eyes on a monitor for an extended period of time. The automation
of video analytics allows the insertion of human judgment at the most critical time in the
surveillance process.
7. CONCLUSION AND FUTURE WORK
Now, computer industry accept Big Data as a new challenge for all types of machine automated
systems. There are many issues in storage, management, and retrieval of data known as Big Data.
The main problem is how we can use this data for increasing business and improvement in living
standard of people. In this paper we are discussing the issues, challenges, application as well as
proposing some actionable insight for Big Data. It will motivate researchers for finding
knowledge from the big amount of data available in different forms in different areas.
REFERENCES
[1] Web content available on the link: ―http://www.sas.com/en_us/insights/big-data/what-is-big-
data.html‖ on the dated: 16-08-2015
[2] Web content available on the link: ―http://www-01.ibm.com/software/data/bigdata/what-is-big-
data.html‖ on the dated: 16-08-2015
[3] Web content available on the link: ―http://www.dqindia.com/8-innovative-examples-of-big-data-
usage-in-india/‖ on the dated: 16-08-2015
10. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
50
[4] Web content available on the link: ―http://searchbusinessanalytics.techtarget.com/definition/big-data-
analytics‖ on the dated: 16-08-2015
[5] Web content available on the link: ―https://www.statsoft.com/textbook/text-mining‖ on the dated: 16-
08-2015
[6] Web content available on the link: ―https://www.predictiveanalyticstoday.com/text-analytics‖ on the
dated: 16-08-2015
[7] Web content available on the link: ―https://gigaom.com/2014/01/24/why-video-is-the-next-big-thing-
in-big-data/‖ on the dated: 16-08-2015
[8] Web content available on the link: ―http://searchbusinessanalytics.techtarget.com/definition/social-
media-analytics‖ on the dated: 16-08-2015
[9] Web content available on the link: ‖http://en.wikipedia.org/wiki/E-commerce_in_India#cite_note-
Online_shopping_touched_new_heights_in_India_in_2012-1‖ on the dated: 16-08-2015.
[10] Amir Gandomi , Murtaza Haider, ―Beyond the hype: Big data concepts, methods, and analytics‖,
International Journal of Information Management 35 (2015) 137–144, journal homepage:
www.elsevier.com/locate/ijinfomgt
[11] Radhakrishnan, Ajay Divakaran and Paris Smaragdis, ―AUDIO ANALYSIS FOR SURVEILLANCE
APPLICATIONS‖, 2005 IEEE Workshop on Applications of Signal Processing to Audio and
Acoustics, October 16-19, 2005, New Paltz, NY.
[12] C.L. Philip Chen, Chun-Yang Zhang, 2014, ―Data-intensive applications, challenges, techniques and
technologies: A survey on Big Data‖, Contents lists available at ScienceDirect Information Sciences,
275 (2014) 314–347
[13] Stephen Kaisler, Frank Armour, J. Alberto Espinosa, William Money, 2013, ―Big Data: Issues and
Challenges Moving Forward‖, 2013 46th Hawaii International Conference on System Scien 1530-
1605/12, 2012 IEEE,
[14] Web content available on the link: ―http://analyticstraining.com/2014/the-power-of-social-media-
analytics/‖ on the dated: 17-10-2015
[15] Edmon Begoli, James Horey, 2012, ―Design Principles for Effective Knowledge Discovery from Big
Data‖, 2012 Joint Working Conference on Software Architecture & 6th European Conference on
Software Architecture, 978-0-7695-4827-2/12, IEEE
[16] Yang Song, Gabriel Alatorre, Nagapramod Mandagere, and Aameek Singh, 2013, ―IEEE
International Congress on Big Data‖, 978-0-7695-5006-0/13, IEEE
AUTHORS
Jai Prakash Verma is associated with Nirma University since 2006. He joined as a
Lecturer in MCA section - Computer Science and Engineering Department. He received
Bachelor in Science (B Sc in PCM) and MCA from University of Rajasthan, Jaipur. He
is currently pursuing his PhD from Charusat University, Changa in the area of Big Data
Analysis. Data Warehousing and Mining is the main area of his expertise. He has been
actively involved in many STTP organized within Nirma university. He is currently
working as Assistant Professor in MCA Section - Computer Science and Engineering Department, Institute
of Technology, Nirma University.
Smita Agrawal received Bachelor in Science (B.Sc. in Chemistry) degree from Gujarat
University, Gujarat, India in 2001 and Master’s Degree in Computer Applications
(M.C.A) from Gujarat Vidhyapith, Gujarat, India in 2004. She is pursuing PhD in
Computer Science and Applications from Charotar University of Science and
Technology (CHARUSAT). She is associated with Computer Science and Engineering
Department of Instutute of Technology - Nirma University since 2009. Her research
interests include Parallel Processing, Object Oriented Analysis & Design and Programming Language(s).
11. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.1, February 2016
51
Bankim Patel, is currently working as a Director and Professor of Computer Science at
Shrimad Rajchandra Institute of Management and Computer Application, Uka Tarsadi
University, Bardoli. He has 26 years of experience in research and teaching in Computer
Science and Applications. Having obtained Ph.D. from Veer Narmad South Gujarat
University in 1996, he had guided and produced 10 Ph. D. and 2 M. Phil. Degree holders in
Computer Science. Currently 8 research scholars are working under him for their Ph.D.
degree. He has authored 60+ papers in international/ national reputed journals, 2 articles
and 4 books so far. He has been awarded several awards like best research paper award
from Indian Science Congress, Significant Contribution award from CSI, Indira Gandhi Excellence award
from International Business Council, by Microsoft IT Academy for reorganization of commitment to
student success through excellence in IT education, Vikas Rattan Award etc. He is the Chief Editor of
National Journal of System and Information Technology (NJSIT) and member of editorial board of several
research journals like International Journal of Information and Computing Technology. He has lead the
Editorial committees for bringing out Proceedings of several National conferences on different themes of
Computer, IT and Knowledge management topics. His area of research includes Natural Language
Processing, Intelligent Systems, and Operating systems.
Atul Patel received Bachelor in Science B.Sc (Electronics), M.C.A. Degree from Gujarat
University, India. M.Phil. (Computer Science) Degree from Madurai Kamraj University,
India. He has received his Ph.D degree from S. P. University. Now he is Professor and
Dean, Smt Chandaben Mohanbhai Patel Institute of Computer Applications – Charotar
University of Science and Technology (CHARUSAT) Changa, India. His main research
areas are wireless communication and Network Security.