Technology – The collection of techniques, skills, methods, and procedures used in the production or production of goods or services, is the use of scientific knowledge for practical purposes, especially in the industry.
The technology has many effects. It has helped to develop more advanced economies (including today’s global economy) and allowed the rise of a holiday class. Many technical processes produce unwanted sub-products known as pollution and reduce natural resources to damage the Earth’s environment. Innovations have always influenced the values of society and raised new questions about the ethics of technology. Examples include the rise of perception of efficiency in terms of human productivity and the challenges of bioethics.
DDoS protection: The world is currently undergoing a new dark age called “DDoS”. DDoS (Direct Delivered Targeting) attacks occur when attackers send unusually large packets of data over network connections to sap the servers of their energy and/or bandwidth. Hackers may then attack other systems at the same time in what calls a “pool attack”. An attack can be debilitating for companies and may even result in data loss. Learn more about this subject and DDoS prevention, Mitigation here.
What is the High security of the Website? DDoS Protection – Meaning and Definition.
DDoS attacks are increasing by the day. They cause webmasters to experience poor performance, interruptions, and data centers to experience downtime. They are frustrating to IT staff who must then respond to them. In this context, DDoS Mitigation and prevention Service is a great tool for controlling the damage that these attacks cause.
DDoS Attacks may be carried out by anyone since they are just computer attacks. So, you can get a DDoS Mitigation Service either for yourself or your website(s). While the Internet security team of your company can help in mitigating attacks, a professional service will also be able to deal with attacks on your system.
A web application firewall is one way of achieving DDoS protection against attacker-based attacks. Such a program can filter malicious software, deny access to specific locations, and prevent attackers from obtaining important information or establishing connections to important targets. Some web application firewalls include additional features, such as DDoS Protection, which allows users to specify rules for websites and computer networks. These features may include the blocking of certain words or images or even the blocking of an entire website.
Who provides?
Many companies, organizations, and other entities use DDoS Services to protect their existing network infrastructure from DDoS Attacks. When a DDoS attack occurs, the Internet Service provider (ISP) will respond to the attack within a short period. For instance, if the attacker is using a spamming tool to send thousands of ICQ messages daily to hundreds of thousands of people, the ISP will block the person’s IP address from accessing the Internet for at least 24 hours. This is what happens when the IP address uses by an attacker-based attack.
DDoS and technology
Another possible method of DDoS protection is to block the attacker’s connection from entering the system. This may be done through the use of IP or domain filtering. Some IP filtering can finish through the use of DNS servers. Another IP filtering can be done through IP Channels. Lastly, DDoS attacks can also prevent by using STUN, LSR, or SBCs (state block transactions) to prevent an attacker from sending his attacks via DNS servers or even through the local area network.
DDos Goals:
The goal of DDoS Services is to prevent successful DDoS attacks so that the company’s resources can be used for more productive purposes. Many people think that DDoS Services only needs during a severe DDoS attack and prevention. However, these services are equally important to prevent a DDoS attack as well. Small or medium-sized businesses have the same risk of attack as larger businesses. The difference lies in the number of resources and time available to smaller businesses. And this is where DDoS Services can be of great help to them.
Many businesses make the mistake of thinking that they don’t need DDoS Protection because they don’t have a lot of websites. Unfortunately, a lot of DDoS attackers do have many websites as part of their operation. With just one successful attack, they could cause the death of a small business before it gets the chance to grow and succeed. So even if you have only a few pages, you should consider getting DDoS protected. Only then will you be able to enjoy the fruits of your labor
Market Intelligence Tools: In marketing, a proper understanding of the markets is critical to determining success. It is even more important when selling online because the market can be vast. It is not an easy task handling clients scattered across the world unless you have the latest digital tools.
These tools can help you to gather market intelligence to help you understand how everything works. You may notice that getting some intelligence allows you to prepare for sudden changes and overcome many challenges. With tools like NetBaseQuid, a marketer is sure of tapping into various data sources to know what is developing. Here are a few things you should know when getting started with market intelligence tools.
Get Information About Customers’ Expectations
One of the things that you would expect when gathering information about the market is to know what buyers want. There are those features that customers look for when buying anything. They may also be liking for improvements on existing features. If you do not have marketing intelligence tools, you may never get such information.
However, the digital revolution has changed things a lot, and so all you need is software like NetBaseQuid, and everything will fall in place. You can set up a few functions to ensure that you are alerted about conversations between potential customers so that you know what they expect. You can also take it a step higher and engage them directly in some of the talks.
Know All the Latest Market Trends
If you have been involved in marketing for a long time, whether online or offline, you know that trends determine customers’ actions. Some trends can be so strong that they sweep some products off the shelves. In addition to that, some trends last, while others appear and fade away as fast.
For a marketer, knowing the trend that is likely to drive sales at every particular time can lead to more profits. Through marketing intelligence tools, you can always see how customers are reacting to various trends. You can then use these insights to create a marketing strategy that helps you to get the best out of whatever trend that comes up.
Know The Best Way To Launch Products
If you have just finalized the creation of a fantastic product and you are ready to launch it, a little market intelligence will make things easier for you. You have to understand how customers are likely to receive it. You also need to know the impression that you have to make for the product to be acceptable.
If you can find out how the latest entrants did it to be successful, you have an idea of what to do. You will also know the mistakes that save you from the trouble of having to recall products after launch. Because markets vary, you need to gather intelligence from every new one before venturing there. This should be easy when you have the right digital tools for the job.
Understand the Business Environment You Are Working In
Sometimes, you need intelligence to understand the environment you are working in. It is good to be aware of your surroundings. For instance, knowing that a new company selling products similar to yours has entered the scene can prepare you for the competition. Also, knowing when players in the market are using unethical methods to make deals will respond accordingly. It is all about being aware of your surroundings, and this is made possible by using the best digital tools.
Marketing intelligence can be useful in many other ways. What a marketer should focus on is finding the right tools. Software like NetBaseQuid can provide a variety of features to meet all your expectations.
Getting Started With Market Intelligence Tools Like NetBaseQuid
SAS was redesigned for SAS seventy-six with an open structure that allowed compilers and procedures. The INPUT and IN FILE declarations have been improved so that they can read most of the data codecs used by IBM mainframes. Notification generation has also been added via PUT and FILE statements. The ability to analyze common linear modes was also added, as was the FORMAT process, which allowed developers to customize the appearance of the data. In 1979 SAS 79 added support for the CMS operating system and started the DATASETS procedure.
When I hire new staff for analysis, I look for detailed analytical data and experience with strengths in any of these languages and SQL. If you do one, you can learn the others quite easily, SAS is probably the most different. The hardest half to learn is knowing how to do the data cleansing logic and the idea of using analytics correctly. Should you make a distinction between SAS GUI options? Are you talking about JMP or Enterprise Miner or elections based mainly on programming?
If you are new to the world of knowledge science and unfamiliar with any of these languages, it makes sense not to know whether to learn R, SAS, or Python. According to IDC, SAS is the largest holder of market share in “advanced analysis” with 35.4% of the market in 2013. It is the fifth largest holder of market share for Business Intelligence (BI) software with a share of 6 , 9% and the largest impartial provider. Competitors like Revolution Analytics and Alpine Data Labs advertise their products as significantly cheaper than SAS’s. Learn about Best Data Science Courses In Bangalore
Navigate to:
Name: Bharani kumar Address: 360DigiTMG – Data Science, Data Scientist Course Training in Bangalore 2nd Floor No, Vijay Mansion, 46, 7th Main Rd, Aswathapa Layout, Kalyan Nagar, Bengaluru, Karnataka 560043 Phone: 1800-212-654321 https://www.google.com/maps/search/360digitm+bangalore/@13.0142552,77.6434835,17z
It offers a wide range of statistical functions, has a good graphical interface for people to learn quickly, and provides good technical support. SAS products intended for monitoring and managing IT technology operations are collectively called SAS IT Management Solutions. SAS collects data from various IT assets on efficiency and usage, then creates reports and analytics. SAS performance management merchandise consolidates and provides graphical displays for employee, department, and organizational level key performance indicators (KPIs). The SAS Supply Chain Intelligence suite of products is offered for supply chain needs, such as forecasting product demand, managing distribution and inventory, and optimizing prices.
If a code for a selected step is lost, you will not get a result. I recommend starting with R first and learning other software on top of that, which will definitely add value. SAS is extremely environmentally friendly when you enter information sequentially and access to the database through SQL is properly integrated. The drag and drop interface makes it simple so you can quickly build better statistical models.
I’ve even worked with all three: I’m a licensed core programmer with two college courses. JMP, unlike scheduled and standard E minor, is believed to have these missing strengths with coding and appears to compete extensively with clients. In contrast, R and Python are used by startups and expert companies. R is more prone to tasks related to statistics and data evaluation, because the works associated with R have mentions such as “Data miner”, “Statistician”, “Data analysis supervisor”, etc.
Then for analysis purposes I choose to use R or Python. R and Python have excellent online community support from the Stack-over feed, mailing lists, code, and user-contributed documentation. SAS packages have DATA steps, which retrieve and manipulate information, and PROC steps, which analyze data. Meanwhile, with the increase in massive information, you can count on an increasing number of business analysts and other non-programmers to also arm themselves with the R language.
He found that the initial costs for all three are similar, although he admitted that the initial costs were not necessarily the most effective basis for comparison. SAS business model Data Science Training Institute in Bangalore isn’t weighted as heavily on preliminary fees for its applications, as a substitute focusing on income from annual subscription fees. As of 2011 SAS’s largest set of products is its line for customer intelligence.
Important Things In Data Science Course in Bangalore
It’s time to have a look at every certainly one of your columns to verify your data is homogeneous and clean. As Data Science is an rising field, there’s a plethora of alternatives Best Data Science Courses in Bangalore available world throughout. A Data Scientist is sort of a webmaster, who not only needs to be a jack of all trades but also a grasp of atleast one of many above fields.
In order to execute privacy compliant initiatives, you’ll have to centralize all your data efforts, sources, and datasets into one place or device to facilitate governance. Then, you’ll need to obviously tag datasets and tasks that contain private and/or delicate data and due to this fact would need to be treated in a different way. You’ve probably noticed that even though you have a country characteristic, for example, you’ve got different spellings, or even lacking information.
Even should you’re not fairly there but in your personal information journey or that of your organization, it’s essential to grasp the method so all the events involved will be capable of understand what comes out in the end. Another way of enriching knowledge is by becoming a member of datasets — basically, retrieving columns from one dataset or tab right into a reference dataset. This is a key factor of any evaluation, however it could shortly turn out to be a nightmare when you have an abundance of sources. Luckily, some instruments such as Dataiku allow you to mix data by way of a simplified process, by simply retrieving data or becoming a member of datasets primarily based on specific, fine-tuned criteria.
Create visualizations that may assist anybody understand the developments in knowledge evaluation with ease. You may undergo Data Science Training Institute in Bangalore the webinar recording of “Who is a Data Scientist” the place our professional has defined the matters in an in depth method.
Finally, one crucially important element of information preparation to not overlook is to ensure that your knowledge and your project are compliant with knowledge privacy laws. Personal data privateness and safety is turning into a priority for users, organizations, and legislators alike and it must be one for you from the very begin of your data journey.
Just like some other scientific self-discipline, data scientists all the time need to ask and discover solutions of What, How, Who and Why of the information obtainable to them. They are required to make a clear outlined plan and work in the direction of attaining the outcomes within limited time, effort and money. The main challenge that right now’s Data Scientists face is not to find options to the present enterprise issues but to identify the problems which are most important to the group and its success.
One of the issues that make people fear data and AI essentially the most is that the algorithm isn’t capable of acknowledge bias. As a result, whenever you train your mannequin on biased data, it will interpret recurring bias as a decision to reproduce and never something to correct. Venture Capitalists have never confirmed such an pleasure in investing cash as in the case of data driven start-ups.
Navigate to:
Name: Bharani kumar Address: 360DigiTMG – Data Science, Data Scientist Course Training in Bangalore 2nd Floor No, Vijay Mansion, 46, 7th Main Rd, Aswathapa Layout, Kalyan Nagar, Bengaluru, Karnataka 560043 Phone: 1800-212-654321
How does artificial intelligence work? Artificial intelligence is a branch of computer science that aims to create intelligent machines. What does AI or Artificial Intelligence mean? PPT with Components of Robot; It has become an essential part of the technology industry. Article of Artificial intelligence (AI) is the ability of a computer program or a machine to think and learn. The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Artificial Intelligence: Meaning, Definition, Components with PPT Example.
AI or artificial intelligence a modern approach; As well as, refers to software technologies that make a robot or computer act and think like a human. Some software engineers say that it is only artificial intelligence if it performs as well or better than a human. In this context, when we talk about performance, we mean human computational accuracy, speed, and capacity.
What is Artificial intelligence? It is a theory and development of computer systems that can perform tasks that normally require human intelligence. As well as, Speech recognition, decision-making, visual perception; for example, are features of human intelligence that AI may possess. Translation between languages is another feature.
Meaning and Definition of Artificial Intelligence:
An ideal (perfect) intelligent machine is a flexible agent that perceives its environment and takes actions to maximize its chance of success at some goal or objective. As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition.
For example, optical character recognition is no longer perceived as an exemplar of “artificial intelligence”: it is just a routine technology. At present we use the term AI for successfully understanding human speech, competing at a high level in strategic game systems, self-driving cars, and interpreting complex data. Some people also consider AI a danger to humanity if it continues to progress at its current pace.
The following definition of AI:
First, definition;
“Artificial Intelligence, or AI, is the ability of a computer to act like a human being. It has several applications, including software simulations and robotics.”
Second, definition;
“However, artificial intelligence is most commonly used in video games, where the computer is made to act as another player.”
AI, the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. Also, the term frequently applies to the project of developing systems endowed with the intellectual processes characteristic of humans; such as the ability to reason, discover meaning, generalize, or learn from experience.
One thing we know, John McCarthy came up with the name “Artificial Intelligence” in 1955. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can program to carry out very complex tasks; for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity; there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge.
On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks; so that AI in this limited sense finds in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.
Here is the PPT slide explain What is Artificial Intelligence?
What are the Components of the Robot?
Robot: A machine resembling a human being and able to replicate certain human movements and functions automatically. Also, a robot is a machine designed to execute one or more tasks automatically with speed and precision.
Some of the important components of Robot are as follows:
Manipulator.
Controller.
Sensors.
End-effector, and.
Locomotion Device.
Now, explain;
1] Manipulator:
Just like the human arm, the robot consists of what call a manipulator having several joints and links.
2] Controller:
The digital computer (both the hardware and the software) acts as a controller to the robot. Also, the controller functions in a manner analogous to the human brain. With the help of this controller, the robot can carry out the assigned tasks. As well as, the controller directs and controls the movement of the Manipulator and the end-effector. In other words, the controller controls the robot.
3] Sensors:
Without the data supplied by the sense organs, the brain would be incapable of intelligence. In other words, the controller (the computer) of the robot cannot do any meaningful task, if the robot is not with a component analogous to the sense organs of the human body.
Thus, the fifth and most important component of the robot is the set of sensors. Sensors are nothing but measuring instruments that measure quantities such as position, velocity, force, torque, proximity, temperature, etc.
4] End-effector:
The base of the manipulator is fixed to the base support and at its other free end, the End-effector attaches. As well as, the end-effector expects to perform tasks normally performed by the palm and finger arrangements of the human arm.
5] Locomotion Device:
In the case of Human Beings, the power for the movement of the arm, the palm, and fingers provide by muscles. For the robot, the power for the movement (locomotion) is provided by the motors. Also, the motors used for providing locomotion in robots are of three types depending on the source of energy: Electric, Hydraulic, or Pneumatic.
What does Artificial Intelligence (AI) mean? PPT with Components of Robot, #Pixabay.
What is Knowledge? without knowledge, Artificial Intelligence maybe calls Zero.
By Peter Drucker, The truly revolutionary impact of the Information Revolution is not artificial intelligence, information, or the effect of computers and data processing on decision-making, policymaking, or strategy. Also, the key to continued growth and leadership in the New Economy is not the electronics of computers but the cognitive skills of the “knowledge workers”.
Knowledge may record in the individual brain or stored in the organizational process, products, facilities, systems, and documents. Knowledge is the capacity to act. It is the product of learning, related to human activity, and is more than just a piece of information.
Knowledge assets are the knowledge regarding markets, products, technologies, and organizations, that a business owns or needs to own and which enable its business process to generate profits. What does Employees Stock Option mean? with Motivating Employees.
Companies using data warehousing and its effects, How many Types of Data Warehousing? What are the benefits of using data warehousing? The term data warehouse or data warehousing was first coined by Bill Inmon in the year 1990 which was defined as a “warehouse which is subject-oriented, integrated, time variant and non-volatile collection of data in support of management’s decision-making process”. When referring to data warehousing as subject-oriented, it simply means that the process is giving information about a particular subject rather than the details regarding the on-going operations of the company. It is a blend of technologies and components which allows the strategic use of data. You are read and studying to learn, What do you think of Data Warehousing? Download PDF.
A data warehousing is a technique for collecting and managing data from varied sources to provide meaningful business insights. What do you think of Data Warehousing? Download PDF.
It is electronic storage of a large amount of information by a business which is designed for query and analysis instead of transaction processing. It is a process of transforming data into information and making it available to users in a timely manner to make a difference. Moreover, when data warehousing was referred to as integrated it means that the data or information which are gathered from a number of sources are then all gathered to synthesize a coherent whole.
On the other hand, data warehousing being time variant simply means that the data available were identified during a particular period. Lastly, data warehousing as being non-volatile means that the data is stable and when a new data is added to the system, the old data are never removed, instead they just remain there and this enables the organization to be able to give the management consistency in their business. In the existence of modern times with the advent of technological advancements inevitably affecting the businesses in major ways, there has also been a development and emergence of new measures, practices, and techniques which used technology to be able to provide an unwavering solution to the problems in the organization with regards to the level and kind of information that the organization needs to be able to survive and prosper amidst the increasing competition in the market.
Undeniably, one of this techniques and practices refers to the emergence of data warehousing as a tool for helping today’s businesses to be able to manage competition and the turbulent economic condition. The birth f the concept of data warehousing can be contributed to various researches and studies which were conducted in the past to provide various organizations with the means of getting information in a manner which is efficient, effective, and flexible. The data warehousing which is known today among the corporate practice is not what it was when it started almost two decades ago. The practice of data warehousing nowadays is a result of the experiences and technologies in the last twenty years. Bill Inmon and Ralph Kimball are two of the heavyweights when it comes to data warehousing.
However, although their names are known in this field, these two scholars have two varying views with regards to data warehousing. The paradigm which was illustrated by Inmon holds that the data warehouse forms only a part of the general business intelligence system. On the other hand, the paradigm of Kimball assumes that the data warehouse is a conglomerate of all the data in the organization.
Other researchers assume that there is no right or wrong theories among the two assumptions from the two heavy weighs in data warehousing. However, most of them support the notion of Kimball’s paradigm. They believe that most data warehouses started only as efforts from various departments starting with what they call as data marts until they develop and evolve to become a data warehouse. Furthermore, data warehousing has been heralded as one of the sustainable solutions to management information and dilemma and such also provide the organization and environment which entails various benefits if they are practiced in the right way and if the perspectives are directed towards the right goal.
The process of data warehousing is said to have the intention of providing an architectural model which can best provide an illustration of the flow data from the systems regarding the operation of the decision support environments. However, according to the same author, one problem stems down from the data warehousing technique – that is such a system is said to be too expensive to be affordable for some organizations or businesses.
It is undeniable that data warehousing continues to attract interest, it is also undeniable that many projects are failing to deliver the expectations from what they are supposed to deliver and they still prove to be too high of accost to be handled by some businesses. However, to be able to justify this relatively high cost, it has been said that organizations should look at the long-term benefit of the warehouse rather than simply looking at the short-term benefits that such an offer. Moreover, data warehousing is also said to be designed to be able to support ad-hoc data analysis, inquiry and reporting by end-users, without programmers, interactively and online.
There are some key factors which can make the data warehousing practice a success among different organizations. One of the key ingredients to the success of the practice is to make the management, especially the higher management, aware and conscious of all the benefits which this tool entails and what can data warehousing do to improve the performance of the business.
Another key to the success of data warehousing is choosing the right people to make it happen. By choosing the right people, the contribution of individual minds should be recognized to form a synthesis and a greater whole. Training strategy, the right structure or architecture, a sustainable mission statement, showing early benefits, ensuring scalability, understanding how important is the quality of data and using only proven and effective methodology are some of the other key ingredients to make data warehousing a successful practice, Data Warehousing file Download in PDF.
Why needs Data warehousing?
The data warehousing is needed for all types of these users like:
If the user wants fast performance on a huge amount of data which is a necessity for reports, grids or charts, then Data warehouse proves useful.
Decision makers who rely on the mass amount of data.
It is also used by the people who want simple technology to access the data.
It also essential for those people who want a systematic approach to making decisions.
The data warehouse is a first step If you want to discover ‘hidden patterns’ of data-flows and groupings.
Users who use customized, complex processes to obtain information from multiple data sources.
The Companies using data warehousing and its effects.
An example of a known company which uses data warehousing is WalMart. Being the world’s largest retailer, many say that the company should be also the organization with the largest data warehouse which is going to serve as the database of its inventory and all transactions related to their business performance. The data warehousing also has a big implication on the business of WalMart.
According to the management of the world’s largest retailer, more than any other purpose, their data warehouse is helping them to be able to make decision support systems between the company and its various suppliers. Aside from that, another implication of data warehousing on WalMart is that it enables the suppliers to be able to access a large amount of online information and data which will be helpful with their suppliers in terms of improving their operations.
One example of companies using and reaping the benefits of adapt warehousing will be various pharmaceutical companies, or on a larger scale, the general healthcare industry. For most of the pharmaceutical businesses which are under operation, they were able to acknowledge the fact that they lack a sustainable focus on their promotional practices, resulting in diffused sales efforts. With that, they regard that data warehousing technique has a big implication in their business because they regard such as the best medicine and remedy for the aforementioned problem.
They are even using data warehousing to be able to attain a sustainable competitive edge against other businesses in the industry. In the case of pharmaceutical companies, it has an implication also in the marketing department. Data warehousing helps the marketing department, through various information contained, to come up with promotional and marketing activities which can yield the maximum results. Moreover, data warehousing also has an implication on the human resources department of the organizations because they can also help in the effective allocation of people and resources.
How many Types of Data Warehousing?
There are three main types of Data Warehousing are:
Enterprise Data Warehousing: Enterprise Data Warehouse is a centralized warehouse. It provides decision support service across the enterprise. It offers a unified approach to organizing and representing data. It also provides the ability to classify data according to the subject and give access according to those divisions.
Operational Data Store: Operational Data Store, which is also called ODS, are nothing but data store required when neither Data warehouse nor OLTP systems support organizations reporting needs. In ODS, Data warehouse is refreshed in real time. Hence, it is widely preferred for routine activities like storing records of the Employees.
Data Mart: A data mart is a subset of the data warehouse. It specially designed for a particular line of business, such as sales, finance, sales or finance. In an independent data mart, data can collect directly from sources.
What are the benefits of using data warehousing?
Some of the benefits of data warehousing that it offers include the fact that it has a relative orientation on the subject area, it has the ability to provide an integration of data which were retrieved from diverse and multiple sources, it allows data analysis from time to time, it adds ad hoc inquiry and reporting, it provides decision makers with the capabilities to analyze, it relieves the IT from information development.
It has the ability to provide better performance for complex analytical queries, it relieves the burden of processing databases which are based on transactions, it allows a planning process that is perpetual and continuous, and lastly, it converts corporate data to make them strategic information which can help them in planning for a better performance of the organization.
Another benefit of data warehousing is that it enables and it helps different organizations in the strategic decision making resulting into the formulation of strategic decisions which are geared towards enabling a better business performance and yielding better results.
It can be assumed that most data warehousing practices are not intended for strategic decision making because they are normally used for post-monitoring of decisions regarding how effective they are. Nonetheless, it should not be also disregarded that data warehousing, can also be sued for strategic decision making and can be used profitably.
Another benefit of data warehousing is that it enables the user to have unlimited access to a relatively very large amount of enterprise information which can be used to potentially solve a large number of enterprise problems which can even be used to increase the profitability of the company. A very well-designed data warehouse can yield a greater return-on-investment with unlimited benefits had the ability to better assess the risks associated with the organization. Fully read on PDF file and download.
An arrangement of networked computers in which data processing capabilities are spread across the network. In DDP, specific jobs are performed by specialized computers which may be far removed from the user and/or from other such computers. This arrangement is in contrast to ‘centralized’ computing in which several client computers share the same server (usually a mini or mainframe computer) or a cluster of servers. DDP provides greater scalability, but also requires more network administration resources.
Understanding of Distributed Data Processing (DDP)
Distributed database system technology is the union of what appear to be two diametrically opposed approaches to data processing: database system and computer network technologies. The database system has taken us from a paradigm of data processing in which each application defined and maintained its own data to one in which the data is defined and administered centrally. This new orientation results in data independence, whereby the application programs are immune to changes in the logical or physical organization of the data. One of the major motivations behind the use of database systems is the desire to integrate the operation data of an enterprise and to provide centralized, thus controlled access to that data. The technology of computer networks, on the other hand, promotes a mode of that work that goes against all centralization efforts. At first glance, it might be difficult to understand how these two contrasting approaches can possibly be synthesized to produce a technology that is more powerful and more promising than either one alone. The key to this understanding is the realization that the most important objective of the database technology is integration, not centralization. It is important to realize that either one of these terms does not necessarily imply the other. It is possible to achieve integration with centralization and that is exactly what at distributed database technology attempts to achieve.
The term distributed processing is probably the most used term in computer science for the last couple of years. It has been used to refer to such diverse system as multiprocessing systems, distributed data processing, and computer networks. Here are some of the other term that has been synonymous with distributed processing distributed/multi-computers, satellite processing /satellite computers, back-end processing, dedicated/special-purpose computers, time-shared systems and functionally modular system.
Obviously, some degree of the distributed processing goes on in any computer system, ever on single-processor computers, starting with the second-generation computers, the central processing. However, it should be quite clear that what we would like to refer to as distributed processing, or distributed computing has nothing to do with this form of distribution of the function of function in a single-processor computer system. Web Developer’s Workflow Become Much Easier with this Innovative Gadgets.
A term that has caused so much confusion is obviously quite difficult to define precisely. The working definition we use for a distributed computing systems states that it is a number of autonomous processing elements that are interconnected by a computer network and that cooperate in performing their assigned tasks. The processing elements referred to in this definition is a computing device that can execute a program on its own.
One fundamental question that needs to be asked is: Distributed is one thing that might be distributed is that processing logic. In fact, the definition of a distributed computing computer system give above implicitly assumes that the processing logic or processing elements are distributed. Another possible distribution is according to function. Various functions of a computer system could be delegated to various pieces of hardware sites. Finally, control can be distributed. The control of execution of various task might be distributed instead of being performed by one computer systems, from the view of distributed instead of being the system, these modes of distribution are all necessary and important. Strategic Role of e-HR (Electronic Human Resource).
A distributed computing system can be classified with respect to a number of criteria. Some of these criteria are as follows: degree of coupling, an interconnection structure, the interdependence of components, and synchronization between components. The degree of coupling refers to a measure that determines closely the processing elements are connected together. This can be measured as the ratio of the amount of data exchanged to the amount of local processing performed in executing a task. If the communication is done a computer network, there exits weak coupling among the processing elements. However, if components are shared we talk about strong coupling. Shared components can be both primary memory or secondary storage devices. As for the interconnection structure, one can talk about those case that has a point to point interconnection channel. The processing elements might depend on each other quite strongly in the execution of a task, or this interdependence might be as minimal as passing message at the beginning of execution and reporting results at the end. Synchronization between processing elements might be maintained by synchronous or by asynchronous means. Note that some of these criteria are not entirely independent of the processing elements to be strongly interdependent and possibly to work in a strongly coupled fashion.
First, know about What is Agile? Agile has been the buzzword in project management for about a decade, and with good reason. Agile is actually an umbrella term over several project management approaches that are characterized by their ability to allow project teams to respond to changing requirements and priorities by using incremental work packages.While all agile methods have common characteristics, each agile method has unique processes that set it apart. Let’s look at how each method is used with Charlie’s team, who is developing a new software game. What is Agile Methodology?
Agile software development methodology is a process for developing software (like other software development methodologies Waterfall model, V-Model, Iterative model etc.) However, Agile methodology differs significantly from other methodologies. In English, Agile means ‘ability to move quickly and easily’ and responding swiftly to change – this is a key aspect of Agile software development as well.
“Agile Development” is an umbrella term for several iterative and incremental software development methodologies. The most popular agile methodologies include Extreme Programming (XP), Scrum, Crystal, Dynamic Systems Development Method (DSDM), Lean Development, and Feature-Driven Development (FDD). Learning Development and Exercise of Self-Efficacy Over the Lifespan!
Engineering methodologies required a lot of documentation thereby causing the pace of development to slow down considerably. Agile Methodologies evolved in the 1990s to significantly eliminate this bureaucratic nature of engineering methodology. It was part of developer’s reaction against “heavyweight” methods, who desired to drift away from traditional structured, bureaucratic approaches to software development and move towards more flexible development styles. They were called the ‘Agile’ or ‘Light Weight’ methods and were defined in 1974 by Edmonds in a research paper.
An agile methodology is an approach to project management, typically used in software development. It refers to a group of software development methodologies based on iterative development. Requirements and solutions evolve through cooperation between self-organizing cross-functional teams, without concern for any hierarchy or team member roles. It promotes teamwork, collaboration, and process adaptability throughout the project life-cycle with increased face-to-face communication and a reduced amount of written documentation.
Agile methods break tasks into small increments with no direct long-term planning. Every aspect of development is continually revisited throughout the lifecycle of a project by way of iterations (also called sprints). Iterations are short time frames (“timeboxes”) that normally last 1-4 weeks. This “inspect-and-adapt” approach significantly reduces both development costs and time to market. Each iteration involves working through a complete software development cycle characterized by planning, requirements analysis, design, coding, unit testing, and acceptance testing. This helps minimize overall risk and quicker project adaptability. While iteration may not have enough functionality necessary for a market release, the aim is to be ready for a release (with minimal bugs) at the end of each iteration.
Typically, the team size is small (5-9 people) to enable easier communication and collaboration. Multiple teams may be required for larger developmental efforts which may also require a coordination of priorities across teams. Agile methods emphasize more face-to-face communication than written documents when the team is in the same location. However, when a team works at different locations, daily contact is maintained through video conferencing, e-mail, etc. The progress made in terms of the work done today, work scheduled for tomorrow and the possible roadblocks are discussed among the team members in brief sessions at the end of each working day. Besides, agile developmental efforts are supervised by a customer representative to ensure alignment between customer needs and company goals. New Roles of Human Resource Management in Business Development.
Software Development was initially based on coding and fixing. That worked well for smaller software, but as the size and complexities of software grew a need for a proper process was felt because the debugging and testing of such software became extremely difficult. This gave birth to the Engineering Methodologies. The methodologies became highly successful since it structured the software development process. One of the most popular models that emerged was the Software Development Life Cycle (SDLC) that developed information systems in a very methodical manner.Waterfall method is one of the most popular examples of Engineering or the SDLC methodology. A paper published by Winston Royce in 1970 introduced it as an idea. It was derived from the hardware manufacture and construction strategies that were in practice during the 1970s. The relationship of each stage to the others can be roughly described as a waterfall, where the outputs from a specific stage serve as the initial inputs for the following stage. During each stage, additional information is gathered or developed, combined with the inputs, and used to produce the stage deliverables. It is important to note that the additional information is restricted in scope; “new ideas” that would take the project in directions not anticipated by the initial set of high-level requirements are not incorporated into the project. Rather, ideas for new capabilities or features that are out-of-scope are preserved for later consideration.
Learn, RFID (Radio Frequency Identification), Meaning and Definition!
Radio Frequency Identification (RFID) In past few recent years, the automatic identification techniques have become quite more than popular and they have also find their places into the core of service industries, manufacturing companies, aviation, clothing, transport systems and much more. And, it’s pretty clear by this point of time that the automated identification technology especially RFID, is highly helpful in providing information regarding the timings, location and even more intense information about people, animals, goods etc. in transit. RFID is responsible for storage of large amount of data and is reprogrammable also as in contrast with its counterpart barcodes automatic identification technology.
#Meaning of RFID!
“Radio-frequency identification (RFID) uses electromagnetic fields to automatically identify and track tags attached to objects. The tags contain electronically stored information. Passive tags collect energy from a nearby RFID reader’s interrogating radio waves. Active tags have a local power source such as a battery and may operate at hundreds of meters from the RFID reader. Unlike a barcode, the tag need not be within the line of sight of the reader, so it may be embedded in the tracked object. RFID is one method for Automatic Identification and Data Capture (AIDC).”
In everyday life, the most common form of an electronic data-carrying device if often a smartcard which is probably based upon the contact field. But, this kind of a contact oriented card is normally impractical and less flexible to use. On the contrary, if we think of a contactless card with contactless data transferring capabilities, it would be far more flexible. This communication happens between the data carrying device and its reader. Now, this situation may further appear as ideal if it so happens that the power for the data carrying device comes from the reader by making use of the contactless technology. Because of this specific kind of power transferring and data carrying procedures, the contactless automatic identification systems are termed as Radio frequency Identification Systems.
What is Radio Frequency Identification (RFID)?
Definition: The term RFID stands for Radio Frequency Identification. Radio stands for invocation of the wireless transmission and propagation of information or data. For operating RFID devices, Frequency defines spectrum, may it be low, high, ultra high and microwave, each with distinguishing characteristics. Identification relates to identify the items with the help of various codes present in a data carrier (memory-based) and available via radio frequency reading. The RFID is a term which is used for any device that can be sensed or detected from a distance with few problems of obstruction. The invention of RFID term lies in the origin of tags that reflect or retransmit a radio-frequency signal. RFID makes use of radio frequencies to communicate between two of its components namely RFID tag and the RFID reader. The RFID system can be broadly categorized according to the physical components of frequency and data.
Physical components of the RFID system include, but are not limited to, the following: numerous RFID tags and RFID readers and Computers. The factors associated with the RFID tags are the kind of power source its has, the environment in which it operates, the antenna on the tag for communication with the reader, its corresponding standard, memory, logic applied on the chip and application methods on the tag. The RFID tag refers to a tiny radio device also known as radio barcode, transponder or smart label. This tag is comprised of a simple silicon microchip which is attached to a small flat antenna and mounted on a substrate.
The entire device can then be encapsulated in various materials dependent upon its intended usage. The finished RFID tag can then be attached to an object, typically an item, box or pallet. This tag can then be read remotely to ascertain position, identity or state of an item. The application methods of an RFID tag may take the forms attached, removable, embedded or conveyed. Further, the RFID tags depend upon the power source which may be a battery in case of active-tags and an RFID reader in case of passive tags. In context of the environment in which the tag operates, the role of temperature range and the humidity range comes into picture.
The RFID reader is also referred as interrogator or scanner. Its purpose is to send and receive RF data from tags. The RFID reader factors include its antenna, polarization, protocol, interface and portability. The antenna for communication in case of the RFID reader may be internal or external and its ports may assume the values single or multiple. The polarization in case of an RFID reader may be linear or circular and single or multiple protocols may be used. In an RFID reader, Ethernet, serial, Wi-Fi, USB or other interfaces may be used. Regarding portability associated with the reader, it may be fixed or handheld.
Apart from the RFID tags and readers, host computers are also amongst the part of the physical components of an RFID system. The data acquired by the RFID readers is passed to the host computer which may further run a specialist RFID software, or middleware to filter the data and route it to the correct application to be processed into useful information.
Apart from the physical components of an RFID system, the RFID system may be perceived from the frequency perspective. In RFID systems, the frequency may further be classified according to the signal distance, signal range, reader to tag, tag to reader and coupling. The signal distance includes the read range and the write range. The signal range here in case of RFID systems reflects the various frequency bands i.e. LF, HF, UHF and Microwave. Further, the reader to tag frequency may assume single frequency or multiple frequencies. In case of tag to reader frequency, it may be subharmonic, harmonic or an harmonic.
The data sub classification in RFID systems includes, the security associated with the RFID systems, multi-tag read co-ordination and processing. In the similar context, public algorithm, proprietary algorithm or none are applied for the security associated with the RFID systems. The multi-tag read co-ordination techniques used in the latest RFID systems include SDMA, TDMA, FDMA and CDMA. The processing part is composed of the middleware which further has its own architecture which may assume a single or multi-tier shape and its associated location may be reader or the server.
Basic Information: RFID tags are used in many industries, for example, an RFID tag attached to an automobile during production can be used to track its progress through the assembly line; RFID-tagged pharmaceuticals can be tracked through warehouses; and implanting RFID microchips in livestock and pets allows for positive identification of animals.
Since RFID tags can be attached to cash, clothing, and possessions, or implanted in animals and people, the possibility of reading personally-linked information without consent has raised serious privacy concerns. These concerns resulted in standard specifications development addressing privacy and security issues. ISO/IEC 18000 and ISO/IEC 29167 use on-chip cryptography methods for untraceability, tag and reader authentication, and over-the-air privacy. ISO/IEC 20248 specifies a digital signature data structure for RFID and barcodes providing data, source and read method authenticity. This work is done within ISO/IEC JTC 1/SC 31 Automatic identification and data capture techniques.
In 2014, the world RFID market is worth US$8.89 billion, up from US$7.77 billion in 2013 and US$6.96 billion in 2012. This includes tags, readers, and software/services for RFID cards, labels, fobs, and all other form factors. The market value is expected to rise to US$18.68 billion by 2026.
Learn, Explaining the Different types of Data Mining Model!
Data Mining Models: Basically The data mining model are of two types. First Predictive and, Descriptive. Also learn, How to explain Organizational Culture? Meaning and Definition!
Descriptive Models: The descriptive model identifies the patterns or relationships in data and explores the properties of the data examined. Ex. Clustering, Summarization, Association rule, Sequence discovery etc. Clustering is similar to classification except that the groups are not predefined, but are defined by the data alone. It is also referred to as unsupervised learning or segmentation. It is the partitioning or segmentation of the data in to groups or clusters. The clusters are defined by studying the behavior of the data by the domain experts.
The term segmentation is used in very specific context; it is a process of partitioning of database into disjoint grouping of similar tuples. Summarization is the technique of presenting the summarize information from the data. The association rule finds the association between the different attributes. Association rule mining is a two step process: Finding all frequent item sets, Generating strong association rules from the frequent item sets. Sequence discovery is a process of finding the sequence patterns in data. This sequence can be used to understand the trend.
Predictive Models: The predictive model makes prediction about unknown data values by using the known values. Ex. Classification, Regression, Time series analysis, Prediction etc. Many of the data mining applications are aimed to predict the future state of the data. Prediction is the process of analyzing the current and past states of the attribute and prediction of its future state. Classification is a technique of mapping the target data to the predefined groups or classes, this is a supervise learning because the classes are predefined before the examination of the target data.
The regression involves the learning of function that map data item to real valued prediction variable. In the time series analysis the value of an attribute is examined as it varies over time. In time series analysis the distance measures are used to determine the similarity between different time series, the structure of the line is examined to determine its behavior and the historical time series plot is used to predict future values of the variable.
Model Types Used by Data Mining Technologies
The following represents a sampling of the types of modeling efforts possible using Nuggets the Data Mining Toolkit offered by Data Mining Technologies for the banking and Insurance Industries. Many other model types are used and we would be happy to discuss them in more detail if you contact us. Don’t forget to read it, The Importance Benefits of Corporate Retreats in Business!
Claims Fraud Models
The number of challenges facing the Property and Casualty insurance industry seems to have grown geometrically during the past decade. In the past, poor underwriting results and high loss ratio were compensated by excellent returns on investments. However, the performance of financial markets today is not sufficient to deliver the level of profitability that is necessary to support the traditional insurance business model. In order to survive in the bleak economic conditions that dictate the terms of today’s merciless and competitive market, insurers must change the way they operate to improve their underwriting results and profitability.
An important element in the process of defining the strategies that are essential to ensure the success and profitable results of insurers is the ability to forecast the new directions in which claims management should be developed. This endeavor has become a crucial and challenging undertaking for the insurance industry, given the dramatic events of the past years in the insurance industry worldwide. We can check claims as they arrive and score them as to the likelihood of they are fraudulent. This can results in large savings to the insurance companies that use these technologies.
Customer Clone Models
The process for selectively targeting prospects for your acquisition efforts often utilizes a sophisticated analytical technique called “best customer cloning.” These models estimate which prospects are most likely to respond based on characteristics of the company’s “best customers”. To this end, we build the models or demographic profiles that allow you to select only the best prospects or “clones” for your acquisition programs. In a retail environment, we can even identify the best prospects that are close in proximity to your stores or distribution channels. Customer clone models are appropriate when insufficient response data is available, providing an effective prospect ranking mechanism when response models cannot be built.
Response Models
The best method for identifying the customers or prospects to target for a specific product offering is through the use of a model developed specifically to predict response. These models are used to identify the customers most likely to exhibit the behavior being targeted. Predictive response models allow organizations to find the patterns that separate their customer base so the organization can contact those customers or prospects most likely to take the desired action. These models contribute to more effective marketing by ranking the best candidates for a specific product offering thus identifying the low hanging fruit.
Revenue and Profit Predictive Models
Revenue and Profit Prediction models combine response/non-response likelihood with a revenue estimate, especially if order sizes, monthly billings, or margins differ widely. Not all responses have equal value, and a model that maximizes responses doesn’t necessarily maximize revenue or profit. Revenue and profit predictive models indicate those respondents who are most likely to add a higher revenue or profit margin with their response than other responders.
These models use a scoring algorithm specifically calibrated to select revenue-producing customers and help identify the key characteristics that best identify better customers. They can be used to fine-tune standard response models or used in acquisition strategies.
Cross-Sell and Up-Sell Models
Cross-sell/up-sell models identify customers who are the best prospects for the purchase of additional products and services and for upgrading their existing products and services. The goal is to increase share of wallet. Revenue can increase immediately, but loyalty is enhanced as well due to increased customer involvement.
Attrition Models
Efficient, effective retention programs are critical in today’s competitive environment. While it is true that it is less costly to retain an existing customer than to acquire a new one, the fact is that all customers are not created equal. Attrition models enable you to identify customers who are likely to churn or switch to other providers thus allowing you to take appropriate preemptive action. When planning retention programs, it is essential to be able to identify best customers, how to optimize existing customers and how to build loyalty through “entanglement”. Attrition models are best employed when there are specific actions that the client can take to retard cancellation or cause the customer to become substantially more committed. The modeling technique provides an effective method for companies to identify characteristics of chumers for acquisition efforts and also to prevent or forestall cancellation of customers.
Marketing Effectiveness Creative Models
Often the message that is passed on to the customer is the one of the most important factors in the success of a campaign. Models can be developed to target each customer or prospect with the most effective message. In direct mail campaigns, this approach can be combined with response modeling to score each prospect with the likelihood they will respond given that they are given the most effective creative message (i.e. the one that is recommended by the model). In email campaigns this approach can be used to specify a customized creative message for each recipient.
