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Distributed Digital Artifacts on the Semantic WebEditor IJCATR
Distributed digital artifacts incorporate cryptographic hash values to URI called trusty URIs in a distributed environment
building good in quality, verifiable and unchangeable web resources to prevent the rising man in the middle attack. The greatest
challenge of a centralized system is that it gives users no possibility to check whether data have been modified and the communication
is limited to a single server. As a solution for this, is the distributed digital artifact system, where resources are distributed among
different domains to enable inter-domain communication. Due to the emerging developments in web, attacks have increased rapidly,
among which man in the middle attack (MIMA) is a serious issue, where user security is at its threat. This work tries to prevent MIMA
to an extent, by providing self reference and trusty URIs even when presented in a distributed environment. Any manipulation to the
data is efficiently identified and any further access to that data is blocked by informing user that the uniform location has been
changed. System uses self-reference to contain trusty URI for each resource, lineage algorithm for generating seed and SHA-512 hash
generation algorithm to ensure security. It is implemented on the semantic web, which is an extension to the world wide web, using
RDF (Resource Description Framework) to identify the resource. Hence the framework was developed to overcome existing
challenges by making the digital artifacts on the semantic web distributed to enable communication between different domains across
the network securely and thereby preventing MIMA.
Distributed Digital Artifacts on the Semantic WebEditor IJCATR
Distributed digital artifacts incorporate cryptographic hash values to URI called trusty URIs in a distributed environment
building good in quality, verifiable and unchangeable web resources to prevent the rising man in the middle attack. The greatest
challenge of a centralized system is that it gives users no possibility to check whether data have been modified and the communication
is limited to a single server. As a solution for this, is the distributed digital artifact system, where resources are distributed among
different domains to enable inter-domain communication. Due to the emerging developments in web, attacks have increased rapidly,
among which man in the middle attack (MIMA) is a serious issue, where user security is at its threat. This work tries to prevent MIMA
to an extent, by providing self reference and trusty URIs even when presented in a distributed environment. Any manipulation to the
data is efficiently identified and any further access to that data is blocked by informing user that the uniform location has been
changed. System uses self-reference to contain trusty URI for each resource, lineage algorithm for generating seed and SHA-512 hash
generation algorithm to ensure security. It is implemented on the semantic web, which is an extension to the world wide web, using
RDF (Resource Description Framework) to identify the resource. Hence the framework was developed to overcome existing
challenges by making the digital artifacts on the semantic web distributed to enable communication between different domains across
the network securely and thereby preventing MIMA.
computer notes - Introduction to data structuresecomputernotes
Prepares the students for (and is a prerequisite for) the more advanced material students will encounter in later courses. Data structures organize data Þ more efficient programs
The decision tree is one of the topics of the Bigdata analytics which is a subject of 8th sem CSE students. Book referred is data analytics by Anil Maheshwari.
Python software development provides ease of programming to the developers and gives quick results for any kind of projects. Suma Soft is an expert company providing complete Python software development services for small, mid and big level companies. It holds an expertise for 19 years and is backed up by a strong patronage. To know more- https://www.sumasoft.com/python-software-development
As insurance companies are highly regulated, the use of real data for testing and developmen t may be putting them at risk of non-compliance with international regulations. Research has also found that customer loyalty towards insurance companies is dependent upon the perception that the institution is taking every measure to protect the customers’ personal financial information.This whitepaper explores the various data masking techniques and the best practices of protecting sensitive data.
The volume of Big data is increasing in gigabytes day by day which are hard to make sense and difficult to analyze. The challenges of big data are capturing, storing, searching, sharing, analysis and visualization of these datasets. Big data leads to clutter in their visualization. Clutter is a crowded or disordered collection of graphical entities in information visualization. It can blur the structure of data. In this paper, we present the concept of clutter based dimension reduction. Our purpose is to reduce clutter without reducing information content or disturb data in any way. Dimension reduction is a technique that can significantly reduce the dimensions of the datasets. Dimensionality reduction is useful in visualizing data, discovering a compact representation, decreasing computational processing time and addressing the curse of dimensionality of high-dimensional spaces.
Big data is to be implemented in as full way in real-time; it is still in a research. People
need to know what to do with enormous data. Insurance agencies are actively participating for the
analysis of patient's data which could be used to extract some useful information. Analysis is done in
term of discharge summary, drug & pharma, diagnostics details, doctor’s report, medical history,
allergies & insurance policies which are made by the application of map reduce and useful data is
extracted. We are analysing more number of factors like disease Types with its agreeing reasons,
insurance policy details along with sanctioned amount, family grade wise segregation.
Keywords: Big data, Stemming, Map reduce Policy and Hadoop.
Informatica Data Modelling : Importance of Conceptual ModelsZaranTech LLC
50-55 hours Training + Assignments + Actual Project Based Case Studies
All attendees will receive,
Assignment after each module, Video recording of every session
Notes and study material for examples covered.
Access to the Training Blog & Repository of Materials
Nature Inspired Models And The Semantic WebStefan Ceriu
In this paper we present a series of nature inspired models used as alternative solutions for Semantic Web concerns. Some of the methods presented in this article perform better than classic algorithms by enhancing response time and computational costs. Others are just proof of concept, first steps towards new techniques that will improve their respective field. The intricate nature of the Semantic Web urges the need for faster, more intelligent algorithms and nature inspired models have been proven to be more than suitable for such complex tasks.
A CASE STUDY OF INNOVATION OF AN INFORMATION COMMUNICATION SYSTEM AND UPGRADE...ijaia
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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Dear students get fully solved assignments
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
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computer notes - Introduction to data structuresecomputernotes
Prepares the students for (and is a prerequisite for) the more advanced material students will encounter in later courses. Data structures organize data Þ more efficient programs
The decision tree is one of the topics of the Bigdata analytics which is a subject of 8th sem CSE students. Book referred is data analytics by Anil Maheshwari.
Python software development provides ease of programming to the developers and gives quick results for any kind of projects. Suma Soft is an expert company providing complete Python software development services for small, mid and big level companies. It holds an expertise for 19 years and is backed up by a strong patronage. To know more- https://www.sumasoft.com/python-software-development
As insurance companies are highly regulated, the use of real data for testing and developmen t may be putting them at risk of non-compliance with international regulations. Research has also found that customer loyalty towards insurance companies is dependent upon the perception that the institution is taking every measure to protect the customers’ personal financial information.This whitepaper explores the various data masking techniques and the best practices of protecting sensitive data.
The volume of Big data is increasing in gigabytes day by day which are hard to make sense and difficult to analyze. The challenges of big data are capturing, storing, searching, sharing, analysis and visualization of these datasets. Big data leads to clutter in their visualization. Clutter is a crowded or disordered collection of graphical entities in information visualization. It can blur the structure of data. In this paper, we present the concept of clutter based dimension reduction. Our purpose is to reduce clutter without reducing information content or disturb data in any way. Dimension reduction is a technique that can significantly reduce the dimensions of the datasets. Dimensionality reduction is useful in visualizing data, discovering a compact representation, decreasing computational processing time and addressing the curse of dimensionality of high-dimensional spaces.
Big data is to be implemented in as full way in real-time; it is still in a research. People
need to know what to do with enormous data. Insurance agencies are actively participating for the
analysis of patient's data which could be used to extract some useful information. Analysis is done in
term of discharge summary, drug & pharma, diagnostics details, doctor’s report, medical history,
allergies & insurance policies which are made by the application of map reduce and useful data is
extracted. We are analysing more number of factors like disease Types with its agreeing reasons,
insurance policy details along with sanctioned amount, family grade wise segregation.
Keywords: Big data, Stemming, Map reduce Policy and Hadoop.
Informatica Data Modelling : Importance of Conceptual ModelsZaranTech LLC
50-55 hours Training + Assignments + Actual Project Based Case Studies
All attendees will receive,
Assignment after each module, Video recording of every session
Notes and study material for examples covered.
Access to the Training Blog & Repository of Materials
Nature Inspired Models And The Semantic WebStefan Ceriu
In this paper we present a series of nature inspired models used as alternative solutions for Semantic Web concerns. Some of the methods presented in this article perform better than classic algorithms by enhancing response time and computational costs. Others are just proof of concept, first steps towards new techniques that will improve their respective field. The intricate nature of the Semantic Web urges the need for faster, more intelligent algorithms and nature inspired models have been proven to be more than suitable for such complex tasks.
A CASE STUDY OF INNOVATION OF AN INFORMATION COMMUNICATION SYSTEM AND UPGRADE...ijaia
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).
Dear students get fully solved assignments
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Dear students get fully solved assignments
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Dear students get fully solved assignments
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Dear students get fully solved SMU MBA assignments
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How do data analysts work with big data and distributed computing frameworks.pdfSoumodeep Nanee Kundu
The era of big data has ushered in a new paradigm for data analysis, presenting unique challenges and opportunities. This article delves into the world of big data analytics and explores how data analysts work with distributed computing frameworks to handle large and complex datasets. We'll discuss the concept of big data, the challenges it poses, and the evolution of distributed computing frameworks. Furthermore, we'll dive into the role of data analysts, their skills and tools, and the practical applications of big data analytics. By the end of this article, readers will have a comprehensive understanding of how data analysts leverage distributed computing frameworks to extract valuable insights from vast datasets.
Toward a System Building Agenda for Data Integration(and Dat.docxjuliennehar
Toward a System Building Agenda for Data Integration
(and Data Science)
AnHai Doan, Pradap Konda, Paul Suganthan G.C., Adel Ardalan, Jeffrey R. Ballard, Sanjib Das,
Yash Govind, Han Li, Philip Martinkus, Sidharth Mudgal, Erik Paulson, Haojun Zhang
University of Wisconsin-Madison
Abstract
We argue that the data integration (DI) community should devote far more effort to building systems,
in order to truly advance the field. We discuss the limitations of current DI systems, and point out that
there is already an existing popular DI “system” out there, which is PyData, the open-source ecosystem
of 138,000+ interoperable Python packages. We argue that rather than building isolated monolithic DI
systems, we should consider extending this PyData “system”, by developing more Python packages that
solve DI problems for the users of PyData. We discuss how extending PyData enables us to pursue an
integrated agenda of research, system development, education, and outreach in DI, which in turn can
position our community to become a key player in data science. Finally, we discuss ongoing work at
Wisconsin, which suggests that this agenda is highly promising and raises many interesting challenges.
1 Introduction
In this paper we focus on data integration (DI), broadly interpreted as covering all major data preparation steps
such as data extraction, exploration, profiling, cleaning, matching, and merging [10]. This topic is also known
as data wrangling, munging, curation, unification, fusion, preparation, and more. Over the past few decades, DI
has received much attention (e.g., [37, 29, 31, 20, 34, 33, 6, 17, 39, 22, 23, 5, 8, 36, 15, 35, 4, 25, 38, 26, 32, 19,
2, 12, 11, 16, 2, 3]). Today, as data science grows, DI is receiving even more attention. This is because many
data science applications must first perform DI to combine the raw data from multiple sources, before analysis
can be carried out to extract insights.
Yet despite all this attention, today we do not really know whether the field is making good progress. The
vast majority of DI works (with the exception of efforts such as Tamr and Trifacta [36, 15]) have focused on
developing algorithmic solutions. But we know very little about whether these (ever-more-complex) algorithms
are indeed useful in practice. The field has also built mostly isolated system prototypes, which are hard to use and
combine, and are often not powerful enough for real-world applications. This makes it difficult to decide what
to teach in DI classes. Teaching complex DI algorithms and asking students to do projects using our prototype
systems can train them well for doing DI research, but are not likely to train them well for solving real-world DI
problems in later jobs. Similarly, outreach to real users (e.g., domain scientists) is difficult. Given that we have
Copyright 0000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for
advertising or promotional purpose ...
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With the surge in modern research focus towards Pervasive Computing, lot of techniques and challenges
needs to be addressed so as to effectively create smart spaces and achieve miniaturization. In the process of
scaling down to compact devices, the real things to ponder upon are the Information Retrieval challenges.
In this work, we discuss the aspects of multimedia which makes information access challenging. An
Example Pattern Recognition scenario is presented and the mathematical techniques that can be used to
model uncertainty are also presented for developing a system that can sense, compute and communicate in
a way that can make human life easy with smart objects assisting from around his surroundings.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
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ASSIGNMENT
Course Code : MS - 07
Course Title : Information Systems for Managers
Assignment Code : MS-07/TMA/SEM - II/2015
Coverage : All Blocks
Note : Attempt all the questions and submit this assignment on or before 31st October, 2015 to
the coordinator of your study centre.
Q. 1. What is the purpose of a computer aided decision support system? What are the major
components of such a system? How does it help the decision making process?
Answer:A Decision Support System (DSS) is a computer-based information system that supports
businessororganizational decision-makingactivities. DSSs serve the management, operations, and
planning levels of an organization (usually mid and higher management) and help people make
decisions about problems that may be rapidly changing and not easily specified in advance - i.e.
Unstructuredand Semi-Structureddecision problems. Decision support systems can be either fully
computerized, human-powered or a combination of both.
While academicshave perceivedDSSasa tool to supportdecisionmakingprocess,DSSuserssee DSS
as a tool tofacilitate organizational processes.[1] Some authorshave extendedthe definition of DSS
to include any system that might support
Q. 2. What are the determinantsofMIS designaccording to Zani?Explain the followingstatement,
“there is a two way relationship between organization and Information Systems”.
Answer:The subject of management information system (MIS) has different meaning for different
people. The concept of MIS has evolved over a period of last two decades or so. The initial
managementinformationsystemswere builttoprocesstransactional dataof an organization and to
produce regular reports. The reports were not targeted and individuals picked the required data
fromthe report.The informationsystemsevolvedfurtherand produced different reports according
to requirements. Instead of user looking through
2. Q. 3. Distinguish between data, information and knowledge. Discuss the importance of
information system for business decisions.
Answer:Knowledge:Firstly,let’slookatKnowledge.Knowledge iswhatwe know.Thinkof this as the
map of the Worldwe buildinside ourbrains.Like aphysical map,ithelpsusknow where thingsare –
but it contains more than that. It also contains our beliefs and expectations. “If I do this, I will
probablygetthat.” Crucially, the brain links all these things together into a giant network of ideas,
memories, predictions, beliefs, etc.
It is from this “map” that we base our decisions, not the real world itself. Our brains constantly
update this map from the signals coming through our eyes
Q. 4. In which phase of system life cycle the following are performed? Defining the problem,
identifying its causes, specifying the solution, and indentifying the information requirements.
Answer:The system lifecycle in systems engineering is an examination of a system or proposed
system that addresses all phases of its existence to include system conception, design and
development, production and/or construction, distribution, operation, maintenance and support,
retirement,phase-outand disposal. A systems development life cycle is composed of a number of
clearly defined and distinct work phases which are used by systems engineers and systems
developers to plan for, design, build, test, and
Q. 5. What do you understand by Artificial Intelligence? What are the factors that highlighted the
need of developing AI? What are the goals of AI?
Answer:Artificialintelligence(AI) isthe intelligence exhibited by machines or software. It is also the
name of the academicfieldof studywhichstudieshow tocreate computers and computer software
that are capable of intelligentbehavior.MajorAIresearchersandtextbooksdefine this field as "the
study and design of intelligent agents", in which
Q. 6. Write short notes on the following:
a) Metadata
Answer:Metadatais"data about data". There are twotypesof metadata(ortwo typesof "metadata
types ") : structural metadata and descriptive metadata. Structural metadata is data about the
containers of data. Descriptive metadata uses individual instances of application data or the data
content.
3. b) Fuzzy Logic
Answer:Fuzzylogicisa formof many-valuedlogicin which the truth values of variables may be any
real numberbetween0and1. By contrast,inBooleanlogic,the truthvaluesof variablesmayonlybe
0 or 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value
may range between completely true and completely false. Furthermore, when linguistic variables
are used, these degrees may be managed by specific functions.
The term "fuzzy logic" was introduced with the 1965 proposal of fuzzy set theory by Lotfi A. Zadeh.
Fuzzy logic has been applied to many fiel
c) Enterprise Information System
Answer:An enterprise information system (EIS) is any kind of information system which improves
the functionsof an enterprise businessprocesses by integration. This means typically offering high
quality of service, dealing with large volumes of data and capable of supporting some large and
possiblycomplex organizationorenterprise.AnEISmustbe able tobe usedbyall parts and all levels
of an enterprise.
d) Open Source Software
Answer:Open-source software(OSS) iscomputersoftware withitssource code made available with
a license in which the copyright holder provides the rights to study, change, and distribute the
software toanyone andfor any purpose. Open-source software maybe developedinacollaborative
publicmanner.Open-source software isthe mostprominentexample of open-source development
and often compared to (technically defined) user-generated content or (legally defined) open-
content movements.
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