Big Data vs. Virtualization

Big Data Information Approaches

Big Data Information Approaches

Globally, organizations are facing challenges emanating from data issues, including data consolidation, value, heterogeneity, and quality. At the same time, they have to deal with the aspect of Big Data. In other words, consolidating, organizing, and realizing the value of data in an organization has been a challenge over the years. To overcome these challenges, a series of strategies have been devised. For instance, organizations are actively leveraging on methods such as Data Warehouses, Data Marts, and Data Stores to meet their data assets requirements. Unfortunately, the time and resources required to deliver value using these legacy methods is a distressing issue. In most cases, typical Data Warehouses applied for business intelligence (BI) rely on batch processing to consolidate and present data assets. This traditional approach is affected by the latency of information.

Big Data

As the name suggests, Big Data describes a large volume of data that can either be structured or unstructured. It originates from business processes among other sources. Presently, artificial intelligence, mobile technology, social media, and the Internet of Things (IoT) have become new sources of vast amounts of data. In Big Data, the organization and consolidation matter more than the volume of the data. Ultimately, big data can be analyzed to generate insights that can be crucial in strategic decision making for a business.

Features of Big Data

The term Big Data is relatively new. However, the process of collecting and preserving vast amounts of information for different purposes has been there for decades. Big Data gained momentum recently with the three V’s features that include volume, velocity, and variety.

Volume: First, businesses gather information from a set of sources, such as social media, day-to-day operations, machine to machine data, weblogs, sensors, and so on. Traditionally, storing the data was a challenge. However, the requirement has been made possible by new technologies such as Hadoop.

Velocity: Another defining nature of Big Data is that it flows at an unprecedented rate that requires real-time processing. Organizations are gathering information from RFID tags, sensors, and other objects that need timely processing of data torrents.

Variety: In modern enterprises, information comes in different formats. For instance, a firm can gather numeric and structured data from traditional databases as well as unstructured emails, video, audio, business transactions, and texts.

Complexity: As mentioned above, Big Data comes from diverse sources and in varying formats. In effect, it becomes a challenge to consolidate, match, link, cleanse, or modify this data across an organizational system. Unfortunately, Big Data opportunities can only be explored when an organization successfully correlates relationships and connects multiple data sets to prevent it from spiraling out of control.

Variability: Big Data can have inconsistent flows within periodic peaks. For instance, in social media, a topic can be trending, which can tremendously increase collected data. Variability is also common while dealing with unstructured data.

Big Data Potential and Importance

The vast amount of data collected and preserved on a global scale will keep growing. This fact implies that there is more potential to generate crucial insights from this information. Unfortunately, due to various issues, only a small fraction of this data actually gets analyzed. There is a significant and untapped potential that businesses can explore to make proper and beneficial use of this information.

Analyzing Big Data allows businesses to make timely and effective decisions using raw data. In reality, organizations can gather data from diverse sources and process it to develop insights that can aid in reducing operational costs, production time, innovating new products, and making smarter decisions. Such benefits can be achieved when enterprises combine Big Data with analytic techniques, such as text analytics, predictive analytics, machine learning, natural language processing, data mining and so on.

Big Data Application Areas

Practically, Big Data can be used in nearly all industries. In the financial sector, a significant amount of data is gathered from diverse sources, which requires banks and insurance companies to innovate ways to manage Big Data. This industry aims at understanding and satisfying their customers while meeting regulatory compliance and preventing fraud. In effect, banks can exploit Big Data using advanced analytics to generate insights required to make smart decisions.

In the education sector, Big Data can be employed to make vital improvements on school systems, quality of education and curriculums. For instance, Big Data can be analyzed to assess students’ progress and to design support systems for professors and tutors.

Healthcare providers, on the other hand, collect patients’ records and design various treatment plans. In the healthcare sector, practitioners and service providers are required to offer accurate and timely treatment that is transparent to meet the stringent regulations in the industry and to enhance the quality of life. In this case, Big Data can be managed to uncover insights that can be used to improve the quality of service.

Governments and different authorities can apply analytics to Big Data to create the understanding required to manage social utilities and to develop solutions necessary to solve common problems, such as city congestion, crime, and drug use. However, governments must also consider other issues such as privacy and confidentiality while dealing with Big Data.

In manufacturing and processing, Big Data offers insights that stakeholders can use to efficiently use raw materials to output quality products. Manufacturers can perform analytics on big data to generate ideas that can be used to increase market share, enhance safety, minimize wastage, and solve other challenges faster.

In the retail sector, companies rely heavily on customer loyalty to maintain market share in a highly competitive market. In this case, managing big data can help retailers to understand the best methods to utilize in marketing their products to existing and potential consumers, and also to sustain relationships.

Challenges Handling Big Data

With the introduction of Big Data, the challenge of consolidating and creating value on data assets becomes magnified. Today, organizations are expected to handle increased data velocity, variety, and volume. It is now a business necessity to deal with traditional enterprise data and Big Data. Traditional relational databases are suitable for storing, processing, and managing low-latency data. Big Data has increased volume, variety, and velocity, making it difficult for legacy database systems to efficiently handle it.

Failing to act on this challenge implies that enterprises cannot tap the opportunities presented by data generated from diverse sources, such as machine sensors, weblogs, social media, and so on. On the contrary, organizations that will explore Big Data capabilities amidst its challenges will remain competitive. It is necessary for businesses to integrate diverse systems with Big Data platforms in a meaningful manner, as heterogeneity of data environments continue to increase.

Virtualization

Virtualization involves turning physical computing resources, such as databases and servers into multiple systems. The concept consists of making the function of an IT resource simulated in software, making it identical to the corresponding physical object. Virtualization technique uses abstraction to create a software application to appear and operate like hardware to provide a series of benefits ranging from flexibility, scalability, performance, and reliability.

Typically, virtualization is made possible using virtual machines (VMs) implemented in microprocessors with necessary hardware support and OS-level implementations to enhance computational productivity. VMs offers additional convenience, security, and integrity with little resource overhead.

Benefits of Virtualization

Achieving the economics of wide-scale functional virtualization using available technologies is easy to improve reliability by employing virtualization offered by cloud service providers on fully redundant and standby basis. Traditionally, organizations would deploy several services to operate at a fraction of their capacity to meet increased processing and storage demands. These requirements resulted in increased operating costs and inefficiencies. With the introduction of virtualization, the software can be used to simulate functionalities of hardware. In effect, businesses can outstandingly eliminate the possibility of system failures. At the same time, the technology significantly reduces capital expense components of IT budgets. In future, more resources will be spent on operating, than acquisition expenses. Company funds will be channeled to service providers instead of purchasing expensive equipment and hiring local personnel.

Overall, virtualization enables IT functions across business divisions and industries to be performed more efficiently, flexibly, inexpensively, and productively. The technology meaningfully eliminates expensive traditional implementations.

Apart from reducing capital and operating costs for organizations, virtualization minimizes and eliminates downtime. It also increases IT productivity, responsiveness, and agility. The technology provides faster provisioning of resources and applications. In case of incidents, virtualization allows fast disaster recovery that maintains business continuity.

Types of Virtualization

There are various types of virtualization, such as a server, network, and desktop virtualization.

In server virtualization, more than one operating system runs on a single physical server to increase IT efficiency, reduce costs, achieve timely workload deployment, improve availability and enhance performance.

Network virtualization involves reproducing a physical network to allow applications to run on a virtual system. This type of virtualization provides operational benefits and hardware independence.

In desktop virtualization, desktops and applications are virtualized and delivered to different divisions and branches in a company. Desktop virtualization supports outsourced, offshore, and mobile workers who can access simulate desktop on tablets and iPads.

Characteristics of Virtualization

Some of the features of virtualization that support the efficiency and performance of the technology include:

Partitioning: In virtualization, several applications, database systems, and operating systems are supported by a single physical system since the technology allows partitioning of limited IT resources.

Isolation: Virtual machines can be isolated from the physical systems hosting them. In effect, if a single virtual instance breaks down, the other machine, as well as the host hardware components, will not be affected.

Encapsulation: A virtual machine can be presented as a single file while abstracting other features. This makes it possible for users to identify the VM based on a role it plays.

Data Virtualization – A Solution for Big Data Challenges

Virtualization can be viewed as a strategy that helps derive information value when needed. The technology can be used to add a level of efficiency that makes big data applications a reality. To enjoy the benefits of big data, organizations need to abstract data from different reinforcements. In other words, virtualization can be deployed to provide partitioning, encapsulation, and isolation that abstracts the complexities of Big Data stores to make it easy to integrate data from multiple stores with other data from systems used in an enterprise.

Virtualization enables ease of access to Big Data. The two technologies can be combined and configured using the software. As a result, the approach makes it possible to present an extensive collection of disassociated and structured and unstructured data ranging from application and weblogs, operating system configuration, network flows, security events, to storage metrics.

Virtualization improves storage and analysis capabilities on Big Data. As mentioned earlier, the current traditional relational databases are incapable of addressing growing needs inherent to Big Data. Today, there is an increase in special purpose applications for processing varied and unstructured big data. The tools can be used to extract value from Big Data efficiently while minimizing unnecessary data replication. Virtualization tools also make it possible for enterprises to access numerous data sources by integrating them with legacy relational data centers, data warehouses, and other files that can be used in business intelligence. Ultimately, companies can deploy virtualization to achieve a reliable way to handle complexity, volume, and heterogeneity of information collected from diverse sources. The integrated solutions will also meet other business needs for near-real-time information processing and agility.

In conclusion, it is evident that the value of Big Data comes from processing information gathered from diverse sources in an enterprise. Virtualizing big data offers numerous benefits that cannot be realized while using physical infrastructure and traditional database systems. It provides simplification of Big Data infrastructure that reduces operational costs and time to results. Shortly, Big Data use cares will shift from theoretical possibilities to multiple use patterns that feature powerful analytics and affordable archival of vast datasets. Virtualization will be crucial in exploiting Big Data presented as abstracted data services.

 

Data Warehousing vs. Data Virtualization

Information Management

Information Management

Today, a business heavily depends on data to gain insights into their processes and operations and to develop new ways to increase market share and profits. In most cases, data required to generate the insights are sourced and located in diverse places, which requires reliable access mechanism. Currently, data warehousing and data virtualization are two principal techniques used to store and access the sources of critical data in a company. Each approach offers various capabilities and can be deployed for particular use cases as described in this article.

Data Warehousing

A data warehouse is designed and developed to secure host historical data from different sources. In effect, this technique protects data sources from performance degradation caused by the impact of sophisticated analytics and enormous demands for reports. Today, various tools and platforms have been developed for data warehouse automation in companies. They can be deployed to quicken development, automate testing, maintenance, and other steps involved in data warehousing. In a data warehouse, data is stored as a series of snapshots, where a record represents data at a particular time. In effect, companies can analyze data warehouse snapshots to compare data between different periods. The results are converted into insights required to make crucial business decisions.

Moreover, a data warehouse is optimized for other functions, such as data retrieval. The technology duplicates data to allow database de-normalization that enhances query performance. The solution is further deployed to create an enterprise data warehouse (EDW) used to service the entire organization.

Data Warehouse Information Architecture

Data Warehouse Information Architecture

Features of a Data Warehouse

A data warehouse is subject-oriented, and it is designed to help entities analyze data. For instance, a company can start a data warehouse focused on sales to learn more about sales data. Analytics on this warehouse can help establish insights such as the best customer for the period. The data warehouse is subject oriented since it can be defined based on a subject matter.

A data warehouse is integrated. Data from various sources is first out into a consistent format. The process requires the firm to resolve some challenges, such as naming conflicts and inconsistencies on units of measure.

A data warehouse in nonvolatile. In effect, data entered into the warehouse should not change after it is stored. This feature increases accuracy and integrity in data warehousing.

A data warehouse is time variant since it focuses on data changes over time. Data warehousing discovers trends in business by using large amounts of historical data. In effect, a typical operation in a data warehouse scans millions of rows to return an output.

A data warehouse is designed and developed to handle ad hoc queries. In most cases, organizations may not predict the amount of workload of a data warehouse. Therefore, it is recommendable to optimize the data warehouse to perform optimally over any possible query operation.

A data warehouse is regularly updated by the ETL process using bulk data modification techniques. Therefore, end users cannot directly update the data warehouse.

Advantages of Data Warehousing

The primary motivation for developing a data warehouse is to provide timely information required for decision making in an organization. A business intelligence data warehouse serves as an initial checkpoint for crucial business data. When a company stores its data in a data warehouse, tracking it becomes natural. The technology allows users to perform quick searches to be able to retrieve and analyze static data.

Another driver for companies investing in data warehouses involves integrating data from disparate sources. This capability adds value to operational applications like customer relationship management systems. A well-integrated warehouse allows the solution to translate information to a more usable and straightforward format, making it easy for users to understand the business data.

The technology also allows organizations to perform a series of analysis on data.

A data warehouse reduces the cost to access historical data in an organization.

Data warehousing provides standardization of data across an organization. Moreover, it helps identify and eliminate errors. Before loading data, the solution shows inconsistencies to users and corrects them.

A data warehouse also improves the turnaround time for analysis and report generation.

The technology makes it easy for users to access and share data. A user can conduct a quick search on a data warehouse to find and analyze static data without wasting time.

Data warehousing removes informational processing load from transaction-oriented databases.

Disadvantages of Data Warehousing

While data warehousing technology is undoubtedly beneficial to many organizations, not all data warehouses are relevant to a business. In some cases, a data warehouse can be expensive to scale and maintain.

Preparing a data warehouse is time-consuming since it requires users to input raw data, which has to be achieved manually.

A data warehouse is not a perfect choice for handing unstructured and complex raw data. Moreover, it faces difficulties incompatibility. Depending on the data sources, companies may require a business intelligence team to ensure compatibility is achieved for data coming from sources running distinct operating systems and programs.

The technology requires a maintenance cost to continue working correctly. The solution needs to be updated with latest features that might be costly. Regularly maintaining a data warehouse will need a business to spend more on top of the initial investment.

A data warehouse use can be limited due to information privacy and confidentiality issues. In most cases, businesses collect and store sensitive data belonging to their clients. Viewing it is only allowed to individual employees, which limits the benefits offered by a data warehouse.

Data Warehousing Use Case

There are a series of ways organizations use data warehouses. Businesses can optimize the technology for performance by identifying the type of data warehouse they have.

  1. A data warehouses can be used by an organization that is struggling to report efficiently on business operations and activities. The solution makes it possible to access the required data
  2. A data warehouse is necessary for an organization where data is copied separately by different divisions for analysis in spreadsheets that are not consistent with one another.
  3. Data warehousing is crucial in organizations where uncertainties about data accuracy are causing executives to question the veracity of reports.
  4. A data warehouse is crucial for business intelligence acceleration. The technology delivers rapid data insights to analysts at different scales, concurrency, and without requiring manual tuning or optimization of a database.
Data Virtualization Information Architecture

Data Virtualization Information Architecture

Data Virtualization

Data virtualization technology does not require transfer or storage of data. Instead, users employ a combination of application programming interfaces (APIs) and metadata (data about data) to interface with data in different sources. Users use joined queries to gain access to the original data sources. In other words, data virtualization offers a simplified and integrated view to business data in real-time as requested by business users, applications, and analytics. In effect, the technology makes it possible to integrate data from distinct sources, formats, and locations, without replication. It creates a unified virtual data layer that delivers data services to support users and various business applications.

Data virtualization performs many of the same data integration functions, that is, extract, transform, and load, data replication, and federation. It leverages modern technology to deliver real-time data integration with agility, low cost, and high speed. In effect, data virtualization eliminates traditional data integration and reduces the need for replicated data warehouses and data marts in most cases.

Capabilities and Benefits of Data Virtualization

There are various benefits of implementing data virtualization in an organization.

Firstly, data virtualization allows access and leverage of all information that helps a firm achieve a competitive advantage. The solution offers a unified virtual layer that abstracts the underlying source complexity and presents disparate data sources as a single source.

Data virtualization is cheaper since it does not require actual hardware devices to be installed. In other words, organizations no longer need to purchase and dedicate a lot of IT resources and additional monetary investment to create on-site resources, similar to the one used in a data warehouse.

Data virtualization allows speedy deployment of resources. In this solution, resource provisioning is fast and straightforward. Organizations are not required to set up physical machines or to create local networks or install other IT components. Users have a single point of access to a virtual environment that can be distributed to the entire company.

Data virtualization is an energy-efficient system since the solution does not require additional local hardware and software. Therefore, an organization will not be required to install cooling systems.

Disadvantages of Data Virtualization

Data virtualization creates a security risk. In the modern world, having information is a cheap way to make money. In effect, company data is frequently targeted by hackers. Implementing data virtualization from disparate sources may give an opportunity to malicious users to steal critical information and use it for monetary gain.

Data virtualization requires a series of channels or links that must work in cohesion to perform the intended task. In this cases, all data sources should be available for virtualization to work effectively.

Data Virtualization Use Cases

  • Companies that rely on business intelligence require data virtualization for rapid prototyping to meet immediate business needs. Data virtualization can create a real-time reporting solution that unifies access to multiple internal databases.
  • Provisioning data services for single-view applications, such as in customer service and call center applications require data virtualization.

 

What Is Machine Learning?

Machine Learning

Machine Learning

Machine learning is Artificial Intelligence (AI) which enables a system to learn from data rather than through explicit programming.  Machine learning uses algorithms that iteratively learn from data to improve, describe data, and predict outcomes.  As the algorithms ingest training data to produce a more precise machine learning model. Once trained, the machine learning model, when provided data will generate predictions based on the data that taught the model.  Machine learning is a crucial ingredient for creating modern analytics models.

Why Consilience Is Important?

Tree of knowledge

Tree of knowledge

What is Consilience?

Consilience is the confluence of concepts and/or principles from different disciplines, especially, when forming a comprehensive unifying theory.

Independent Confirmation

Why are some inventions discovered at the same time in different parts of the world? Does this have something to do with the scientific process of “sharing important discoveries?” Generally, scientists believe that they are part of a community of knowledge. Their discoveries do not occur in a vacuum. They must give credit to those who went before and created the foundation for their work. Therefore, when they discover something new, they are required to share it with the entire world. This sharing is part of knowledge evolution. Interestingly enough, it is also key to the World Wide Web. Collaboration is one of the key strengths of the Internet. It is a way to increase overall knowledge of Planet Earth. Science can also increase the strength of their theories through independent confirmation.

Result Conciliation

There are oftentimes prescriptions for the types and numbers of witnesses to accomplish certain legal requirements. Anyone who has completed an experiment understands the importance of result conciliation. A hypothesis is not proven to be true unless it can be repeated by independent sources. This shows that the reality is objective. The word, Consilience was formed by two Latin words – “com” meaning “together” and the suffix “-silence” meaning “jumping.” Therefore, Consilience means “jumping together” or a “convergence of proof from independent sources.” Scientists should use different methods to reach the same conclusion. Business and economics have a similar concept. Just think of the concept of a Recession or Depression. These are officially declared when a variety of indicators are in agreement – stock market, employment, inflation, money supply and so forth.

Knowledge Evolution

Consulting can use the concept of Consilience to teach firms how to follow objective norms. Technology consulting can compare a subjective company’s practices to objective industry norms. The best career development is successful based on objective, independent analysis. The concordance of evidence can help a business create a successful strategy. Consilience is the convergence of evidence from independent sources to prove the validity of a conclusion. Objective corporate success can be achieved by satisfying objective needs of your customers. Business intelligence requires an objective standard, such as Consilience to be useful.

Conclusion

Consilience is important to you because the answer to any given problem may not necessarily come from within your field of expertise and experience. rather, to be truly competitive in an ever in an ever increasing world of knowledge, we need to adopt a broad-scoped renaissance approach to learning and thinking, which folds in other sets of concepts and principles to create the durable solutions for today and tomorrow.

 

Management principles – You can’t Manage what you don’t Measure

Business Intelligence and Analytics

Business Intelligence and Analytics

 

Management and Measurement

You can’t manage what you don’t measure is an old management adage that has been used for many years and while most attribute it to Peter Drucker, some claim that the quote was first used by Dr. W. Edwards Deming, although it is a bone of contention whether or not the quote is used in the correct context.

Irrespective of who said it first, I have always agreed with the principle. Coming from a corporate background where this is one of the management principles often used, I was surprised to learn that there are those that strongly disagree with the statement. This group argues that there are many things being managed at work that aren’t measurable, from the confidence we instill in a new, young manager, to the quality of new hires.

The argument is made that quantity is easy to measure, i.e., how much salespeople sell, how many leads marketing creates, or how many phone calls telemarketing makes, but that quality can’t be measured, i.e., excellent customer service, good technical support, or what differentiates a good consultant from a great one.

What to measure

Many organizations use Key Performance Indicators (KPIs) at multiple levels to measure their success at reaching targets, and will then manage the factors influencing the KPI to get it to where they want it to be. A KPI is a value that is measured and shows how effective a company is in reaching key business goals.

Setting a KPI and measuring a specific value is however not always as straightforward as it might seem. To set a KPI, the underlying business objective needs to be properly understood. In one example, a department manager’s KPI included the volume of sales, measured in dollars. In an effort to improve sales, the manager decided to change the remuneration of her sales reps from a fixed salary to a small, basic salary plus commission on sales made. The idea behind this was to incentivize the work, which would lead to increased sales. In the early months after implementing the change, the sales made by account reps did indeed increase dramatically. The CFO then however discovered that the profit margin on those increased sales was substantially lower than the minimum the company expected. The sales reps were discounting the product to increase sales, resulting in a high commission, but the net effect was that the company made less profit.

It is critical that the company’s objectives are clearly understood by all parties and that a suitable metric is measured to check if the objective is being met.

Can quality be measured?

Those arguing that quality, such as excellent customer service, or good technical support can’t be measured, often express the view that the only way that a company can determine how good their service or support is, is by asking the customer. I agree with that statement, but when you do that, aren’t you measuring these aspects? If 50% of your customers feel that your service and support is good, that is a measure against which you can manage and improve those objectives.

The same can be done for any qualitative metric. It merely becomes a question of what is appropriate to measure, and how to obtain those metrics. Qualitative measures often have to be done indirectly, i.e., you need to measure indirect results rather than direct ones.

 The role of Business Intelligence

With the sheer volume of data available across the business, and with much of it residing in different systems, it becomes very difficult to extract the relevant metrics to measure and improve. This is where Business intelligence or BI comes in.

BI utilizes computer-based techniques to spot, extract, and analyze business data, including things like sales, marketing, and production in order to make substantial improvements. Business Intelligence uses data already collected in the business. It is able to utilize data from such diverse sources as website analytics, accounting systems, customer relationship management (CRM) and email management systems.

A Business Intelligence system can automatically use and analyze all the information from these applications in real-time. This enables companies to quickly see, manage and improve their performance. BI goes further than simply measuring performance so that it can be improved, but also helps identify weaknesses in the company.

When an organization grows to the point where huge volumes of data are involved, analytics are used to examine large and varied data sets to uncover correlations, hidden patterns, customer preferences and market trends; so, organizations can make more-informed business decisions.

Both BI and big data analytics can hugely benefit Organization & Planning within any business. If you have all this information, irrespective of how exactly it was obtained or measured, managing the direction you want to go becomes an informed decision that can be planned for, rather than a guessing game based on ‘gut feel.’

A crucial element that is required in today’s fast-moving world is an organization’s ability to respond rapidly to changes in both the external and internal environment. This is known as Business agility, and it is not possible to do if the business does not measure what is going on inside and around it, and then manages accordingly.

Related References

 

What is Information Management?

Information Management (IM)

Information Management (IM)

Information Management Definition

Information Management (IM) tends to vary a based on your business perspective, but is all the systems, processes, practice (business and technical) within organizations for the creation, use, and disposal of business information to support business operations.

Information Management (IM) Activities

Information Management activities may include, but are not be limited to:

  • Information creation, capture, storage, and disposal
  • The governance of information, practices, meaning and usage
  • Information protection, Regulatory compliance, privacy, and limiting legal liability
  • Technological infrastructure, such as, architecture, strategies and delivery enablement

Related References

 

Information Technology – What is Greer’s Third Law?

Greer's Third Law, Acronyms, Abbreviations, Terms, And Definitions, What is Greer's Third Law

Greer’s Third Law

Greer’s Third Law

A computer program does what you tell it to do, not what you want it to do

Related References

How to stop and restart Cognos Service from Linux command line

stop and restart cognos service from linux command line

stop and restart cognos service from linux command line

I don’t do this very often, but recent had to look this up to help out a project.  Stopping and restarting a Cognos from a Linux command line is relatively simple, just a couple of commands.

  • Log on to the reporting server as Root user or a non-root user with administrative privileges.
  • Find the path to install bin directory.  I use this find command, but you can do what works for you:   find . -name “cogconfig.sh”
  • Launch an and navigate to the bin directory as follows: <Cognos_Home>/bin64
  • Where <Cognos_Home> is the installation location of the Cognos® application.
  • Do the following one or both of the following, according to what you are attempting to do:
    • To start the service, enter the following command: ./cogconfig.sh -s
    • To stop the service, enter the following command: ./cogconfig.sh -stop

 

Related References