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Written by martcon
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Wednesday, 14 April 2010 13:18 |
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Mobile communications have evolved from calls and text messages to Internet browsing and streaming video. These latter services are provided with 3rd generation technology (or 3G for short) by mobile phone operators and with WiFi by broadband providers. 4G (4th Generation) communications are considered to be based on WiMAX technology. WiMAX can be thought of as enhanced WiFi services. The key difference is that where the latter provides hotspots for contained areas such as colleges, shopping centres or cafés, WiMAX hotspots can provide coverage for an entire city.
WiMAX is also known as IEEE 802.16 and is intended for wireless Metropolitan Area Networks (MANs) and can provide access for up to 50km for a fixed WiMAX station. For mobile stations access is provided for up to 15km. WiMAX provides the same types of data rates as WiFi but over a longer distance.
Despite the implication that 4G is a successor to 3G mobile phone technology, WiMAX can essentially be seen as a evolution of WiFi. In fact, 4G is a term that is applied to multiple wireless technologies. Long Term Evolution (LTE) is often considered to be a 4G technology even though it does not comply fully with the standards defined by the ITU (International Telecommunications Union). Despite this, LTE has been adopted by most mobile phone operators in Europe, Asia and North America while WiMAX is seen as a product for niche applications. The LTE market is forecast to be US$11 billion by 2014.
So what does LTE do? Like WiMAX, LTE aims to increase the speed and capacity of mobile networks, focusing on cellular networks in particular. LTE is an evolving standard while most recent releases being proposed as candidate to meet 4G technology requirements.
Currently, then, 4G can be seen as a catchall term for a number of data communication standards that will increase data rates for mobile computing, thus facilitating the evolution of pervasive computing - WiMAX and LTE being the key standards. The question we are going to examine is how 4G technologies can assist in the building of smart ecosystems.
The key value proposition for smart ecosystems is that the rich pool of data that is provided by these systems. However, there is a question as to how this data can be returned to a central Information System. Currently the options are to use 2G or 3G cellular technology or WiFi. With the latter, a local hotspot is required. One can therefore see the advantage of using WiMAX which has a much greater range which smart objects can avail of to relay their data in a timely fashion. One could however argue that for many smart objects such as smart meters 2G or 3G technology would suffice. This is certainly true for text based data such as smart meter readings but may not be sufficient for multimedia data. For example, motes in Wireless Multimedia Sensor Networks (WMSN) can capture data with microphones and cameras and can be used for security applications. Similarly, RFID can also incorporate multimedia to provide services for smart buildings and for time and attendance solutions. GPS can also be used for video tracking and provide real-time video monitoring. These applications all require high data speeds and close proximity to an access point. Given the range provided by WiMAX data could be captured and relayed throughout a wide area while LTE can provide the data rates required for multimedia streaming.
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White Paper On Wind Farms |
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Written by martcon
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Thursday, 08 April 2010 14:30 |
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See our White Paper section (under the Resources menu) for our new white paper on Wind Farm operations. |
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Information Management & Smart Objects Part 3: Business Intelligence & Business Analytics |
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Written by martcon
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Monday, 29 March 2010 12:38 |
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Business Intelligence (BI) is a broad term but is generally accepted to refer to the technologies and techniques used to capture, store and analyse data. The goal of BI is to assist organisations in making decisions. Business Intelligence and Business Analytics are terms that are often used interchangeably but Business Analytics can be thought of as being more applied in nature. To distinguish between the two, BI can be defined as the querying, reporting and presentation of historical data while Business Analytics can be defined as the applied data, statistical and quantitative techniques used to deepen insights into organisational performance and assist in business planning. BI itself is sometimes thought of as simply reporting but provides a much richer suite of tools and techniques than just reports. BI is used to analyse and uncover information about past performance using data stored in a database, or more frequently, a data warehouse.
The most well known technique in Business Analytics is Data Mining. Data Mining is the process of sifting through data to pinpoint the patterns in same. Using Data Mining techniques, data can be classified or grouped and statistical predictions can be made. Data Mining and statistical techniques are often considered to be one and the same but statistics is only one discipline that is used when Data Mining. Data Mining is in fact as a blend of statistics, Artificial Intelligence (AI), Machine Learning and Database Technologies. There are many techniques used in data mining. We will consider the most popular techniques here.
Artificial Neural Networks are modelling techniques based on the learning process of the human brain. Using Neural Networks, learning from existing data can take place and predictions can be derived from this data. Genetic Algorithms are optimization techniques that are based on the concepts of natural evolution. Essentially, it is a search technique that is used to find exact or approximate solutions to optimization and search problems. Decision Trees are tree-shaped structures that represent sets of decisions and provide a set of rules that can be used to predict which data records will have a given outcome. The Nearest Neighbour Method is a machine learning technique that uses the ratio of the expected and observed mean value of the nearest neighbour distances to determine if a data set is clustered. A statistical test of significance of the near neighbour statistic is used to quantify the departure of the pattern from random. Another statistical technique is rule induction which is used to extract rules from data based on statistical significance. Finally, visualisation techniques use graphical software to illustrate the relationships between data.
Data Mining, then, is clearly more than statistical analysis of data. It is the process of analysing data from different perspectives and transforming it into useful information that can be used to improve business decision making, reduce costs and increase revenues. Other techniques used in Business Analytics include simulation, forecasting, optimization and experimental design. Most of these techniques are rooted in the field of Management Science, better known as Operations Research.
As noted, Business Analytics is a distinct field from Business Intelligence. The latter is focused on extracting data on historical performance. One common technique used in Business Intelligence is OLAP (Online Analytical Processing). OLAP is a data structure that is commonly referred to as a cube. Using a data cube, data can be viewed in multiple dimensions e.g. sales in a branch of a store for a particular year is data that can be considered multi-dimensional. Data cubes are usually constructed within Data Warehouses and Data Marts and for this reason BI and Data Warehousing are often viewed as complementary technologies. However, it is not necessary to have a Data Warehouse as the underlying data storage for BI.
Another key technique for BI is querying. Querying is the extraction of information for business decision making from a database or data warehouse. There are many BI querying tools in the marketplace but key determinants of the effectiveness of such tools are that they be database independent, can support an industrial strength database that may consist of millions of rows of data and hundreds of columns, be easy to use and be easy to integrate with a spreadsheet or Enterprise System. Reporting is often considered to be synonymous with BI but it is actually just one facet of the area. Reports can be text-based or graphical and are key tools for Performance Management. In fact, the areas of BI and Corporate Performance Management (CPM) are gradually converging and the tools and techniques used for both disciplines are becoming inextricably linked. IBM Cognos (http://www.iba-it-group.com/en/services/BI/) is just one example of a tool that combines the two areas.
The final area of Business Intelligence we will consider is that of Alert and Exception Reporting. This area makes BI more than just a measurement tool for past performance. BI is also effectively a Business Process Monitoring system which highlights an exceptional event occurring in business operations in near real-time. Rules can be implemented to set up criteria for exceptional events. These events can be managed by the BI system to ensure that appropriate responses are carried out and users can be notified of the alerts through web portal headlines and/or their email system.
As noted, BI and Data Warehousing are technologies that are frequently linked. Extract, Transform and Load (ETL) is sometimes considered as a BI technique but more often relates to Data Warehousing. In essence, ETL is the extraction of data from different sources and systems, the cleansing and reformatting of this data and the loading of this data into another database, data warehouse, data mart or Information System.
There are many vendors in the Business Analytics and Business Intelligence spaces. SAS (http://www.sas.com/), Business Objects (http://www.sap.com/solutions/sapbusinessobjects/index.epx) and Crystal Reports (http://www.crystalreports.com/) are all leading Business Intelligence and/or Business Analytics software. Computer Programmers can also integrate BI tools into their own solutions though the use of tools such as the Eclipse BIRT project for J2EE (see http://www.eclipse.org/birt/phoenix/).
The question we must how consider is how Business Analytics and Business Intelligence are of use to smart objects and smart networks. Smart Objects (RFID, Smart Meters, Wireless Sensor Networks and GPS among others) provide a new pool of data for organisations. Given the potential volumes of devices in smart ecosystems it is clear that analysis tools and techniques are required to query and report on the data produced by these networks. In the case of smart meters and the smart grid, the raw data ultimately determines revenues so BI can be used to assess revenue performance and/or cost reductions for non-utilities. A central system for reports, Key Performance Indicator (KPI) measurement and data analysis simplifies the collaboration and sharing of information from smart ecosystems while the Business Analytics techniques we discussed can provide insight and patterns into the vast volumes of real-time data produced and also enable prediction models to be built.
Smart Objects and smart networks should not exist in isolation in an organisation. The data these devices and networks produce needs to be aligned with the overall goals of the organisation. Business Intelligence and Business Analytics are key tools for the measurement of performance, the setting of objectives and the ability to make business decisions in a timely fashion. The pools of data provided by smart objects can be thus transformed into meaningful information for assessing past performance and guiding future planning and decision making. The data from different smart networks can also be combined by BI and Business Analytics tools to give a better picture of what is occurring within the ecosystem.
In summary, BI and Business Analytics transform the data from smart objects into meaningful information for business and makes this information available to all the key decision makers within the organisation. However, BI tools do not generally have the capacity to capture the data from smart objects themselves. Vertoda Middleware would be required to perform this function. Data is captured and stored as meaningful information in a database or data warehouse. The information produced by Vertoda can then be accessed by any type of BI or Business Analytics system and the benefits described above can be derived. |
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Information Management & Smart Objects Part 2: Enterprise Content Management Systems |
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Written by martcon
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Wednesday, 24 March 2010 14:45 |
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Enterprise Content Management Systems can be difficult to define. Broadly speaking, Enterprise Content Management systems are used to capture, store and present content. The term Content Management System (CMS) is sometimes used to describe systems specifically created for website management. These systems can be considered to be a subset of Enterprise CMS and are more correctly referred to as Web CMS.
A Web CMS provides functionality for creating content and data driven web sites. The principal value of a Web CMS is that users require minimal computing expertise. A typical Web CMS will provide presentation, application and data layers using technologies such as HTML, PHP and MySQL. Users can add, modify and remove content as they see fit using simple WYSIWIG (What You See Is What You Get) editing tools. Fonts and styles can be modified and multimedia content such as pictures and video can be added. This content is added using HTML by the background system but users require no programming knowledge.
Typically, a Web CMS breaks a web site into Sections, Categories and Articles. Sections can consist of one or more Categories while Categories can consist of one or more Articles. This makes Web CMS ideal for complex websites where content is frequently updated. Users can also apply templates so that their website has the same style in every web page. This is much less complex a task than using Cascading Style Sheets (CSS). Menus can also be added to the web site using the CMS. The content and style of the website is typically stored in a database.
The above is a subset of the features offered by a Web CMS. There are both commercial and open source offerings in the marketplace. Joomla! (http://www.joomla.org) is an open source web site that has been used by organisations such as Hardvard University, Citibank and MTV to create Internet and Intranet sites. Similarly, Drupal (http://www.drupal.org) is free software that can be used to create content-driven websites. Commerical offerings in this space include Terminal 4 (http://www.terminalfour.com/) and IBM Lotus Web Content Management (http://www-01.ibm.com/software/lotus/products/webcontentmanagement/).
Web CMS is a well known subset of Enterprise CMS but Document Management Systems (DMS - discussed in the previous blog) can also be considered as a subset of Enterprise CMS. Thus, while a CMS may be a system that simply manages digital content it is clear that there are many facets to consider. At a high level, an Enterprise CMS can be seen as a system that manages the digital content, data and documents for the entire organisation. While this may be a simple definition, in actuality an Enterprise CMS may consists of a myriad of subsystems including Document Management, Records Management, Web Content Management, Digital Asset Management, Portal Content Management and Collaboration (including Web 2.0 collaboration - blogs, forums etc.). We have already considered Document, Records and Web Content Management.
Digital Asset Management refers to the capturing, cataloguing, storage and retrieval of digital assets. These assets have value to an organisation and are often in a multimedia format e.g. digital photos and music. Digital Asset Management can be subcategorised further into the management of brand assets such as logos and product images and library assets such as photo archives. Media Asset Management Systems helps you to organise, search for and retrieve media files while Production Asset Management Systems enables organisations to use media assets throughout the production process in areas such as digital media production. Digital Supply Chains enable the delivery of multimedia content such as video or music from the content provider to the final retailer. This latter system has been recognised as a key system for Apple's delivery of content to its iTunes store.
Enterprise Portals deliver content to different stakeholders in an organisations such as employees, investors and suppliers. Portal Management Systems enable collaboration by providing access to and the sharing of content and documents. Finally, Collaboration Systems (sometimes referred to as Groupware) enables teams and organisation members to share content and work with each other. The recent launch of Google Wave (http://wave.google.com) is just one example of such software. IBM's Lotus Notes (http://www-01.ibm.com/software/lotus/) is probably the most well known product.
The advent of Web 2.0 has led to what's sometimes referred to as Social Software for creating content such as blogs or forums. When used for business, this social software is referred to as Enterprise 2.0 and includes not only collaboration tools for blogging and providing help wikis but also the creation of online communities and the management of the organisation's social network. There are many offerings for Enterprise 2.0 Social Software. IBM's Lotus Connections (http://www-01.ibm.com/software/lotus/products/connections/) is just one of many examples.
As we see here, there are many facets to consider and some vendors provide solutions for specifc categories of Enterprise Content Management. Other software vendors provide Enterprise Content Management Solutions that incorporate all these categories as subsystems and features of their product. Solutions such as Microsoft Sharepoint (http://office.microsoft.com/en-us/sharepointserver/HA101747881033.aspx) and Oracle Universal Content Management (UCM - http://www.oracle.com/products/middleware/content-management/enterprise-content-management.html) provide not only web site content management but also the management of diverse content across the organisation. Alfresco (http://www.alfresco.com/) provide an open source solution for managing content across the enterprise.Alternatively, organisations can develop their own CMS using programming tools such as Java Server Pages (JSPs) and Servlets or Microsoft Active Server Pages (ASP).
The question we will now consider is the role Enterprise Content Management Systems have for the management of data generated by smart objects. We will again roughly divide smart objects into smart meters, wireless sensors, GPS devices and RFID. In the case of smart meters, the data generated to measure consumption of a utility could be captured by Vertoda middleware and transferred to and presented by a Web Content Management System. Consumption data could also be used to drive energy efficiency if it is stored and presented on an Enterprise Portal so that every employee is aware of utility consumption within the organisation as well as the level of consumption in different locations and departments. Indeed, the data captured by smart meters and wireless sensors can be made available throughout the organisation by an Enterprise Content Management System. Smart Meter data is often used to drive 'Green' Policies and needs to be available to many interested stakeholders within the organisation while the data generated by Wireless Sensor Networks (WSNs) can be used to improve monitoring and generate new products and services. Whether the data refers to the measurement of aspects of a physical environment or the detection of an element in that physical environment it will need to be made available to many different interested parties within an organisation. Enterprise Content Management Systems are the ideal mechanism for making these pools of data available while Collaboration Systems can make this data available for sharing by teams in areas such as environmental monitoring. GPS data can be used within the Supply Chain to keep track of the location of a delivery or by the Service Engineering department to monitor the location of a moving asset such as a truck. Again, this data can be used to support decision making and assessing the efficiency of operations and could be provided to an Intranet driven by a Web CMS or a Portal Management System to give finer granularity in the management of operations. Finally, RFID data would be used by Inventory Systems to keep track of goods and stock levels and can provide asset tracking content for a CMS.
Capturing the data from smart objects, transforming this data into meaningful information and making this information available to an Enterprise Content Management System is not a trivial task. Middleware is required to provide Enterprise CMS with this new pool of information. Vertoda Middleware performs these tasks and can be easily integrated with any type of commercial or open source CMS, thus providing organisations with information from smart networks that improves decision making and monitoring of for their core operations. |
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Information Management & Smart Objects Part 1: Document Management Systems |
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Written by martcon
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Wednesday, 10 March 2010 16:45 |
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Information Management is a broad term that is often used to describe different concepts in Information Technology. Here, we will define Information Management as the capturing and transformation of data into information and the dissemination of this information to end users. Over the coming weeks, we will examine three key systems that are used for Information Management: Document Management Systems, Enterprise Content Management Systems and Business Intelligence Systems.
A Document Management System (DMS) is an Information System that manages electronic documents and can also capture and manage images of paper documents. Leading DMS include Documentum (see http://www.emc.com/domains/documentum/index.htm), and the commercial open source Knowledge Tree system (see http://www.knowledgetree.com/) but there are many players in this industry such as the open source OpenKM (see http://www.openkm.com/), Ademero (see http://www.ademero.com/), Documatics (see http://www.documatics.com/) and File Hold (see http://www.filehold.com/). In addition, major players such as Alfresco (see http://www.alfresco.com), Microsoft and Oracle have document management functionality in their Enterprise Content Management System offerings. Each of these players have different strengths and often focus on the particular vagaries of document management for a specific sector such as law or pharmaceuticals.
A typical Document Management System (DMS) manages documents generated within the organisation for their entire lifecycle. A Document Management System specifies the types of documents that can be created within an organisation and provides a storage repository for these documents. The system also provides version control. When a user wants to edit a document it must be checked out from the system. When the user has finished altering the document it must be checked back into the system. The system will store both the old and the new version of this document and provides access to all versions of the document from its inception. A system will also provide templates to be used for each type of document and applies policies so that actions on the document are audited and documents are retained or dsposed of appropriately.
As part of the planning process for document management the stakeholders who will participate in the process of document management must be identified and how the documents are used must be assessed. Documents will then be organised into digital libraries or sites for teams and portals. Over the life of a document it may be moved from one area to another, - for example a document may be moved from a staging area to a public Website when it is published. This workflow is supported by the Document Management System.
The most well known Document Management System is EMC's (see http://www.emc.com) Documentum product which was acquired by the corporation in 2003. Documentum provides the features expected from a typical Document Management System i.e. the management of document content. Check in, check out and version control are also supported. Documentum is a 3-tier, client-server system which is built on top of a relational database (Relational Database Management System or RDBMS).
Document content, format, versions, security and workflows are all stored in a DocBase. A DocBase is essentially a database instance that uses a Database server such as Oracle or MySQL plus files of document content. This content is stored as Binary Large Objects (or BLOBs) within the database or within the file system of the Documentum server. The atrributes relating to the file (creation date, name etc.) are referred to as the file's metadata and are stored within the RDBMS. To query the data, the Documentum product has an extended dialect of SQL called Documentum Query Language (DQL). Software Development Kits (SDKs) and Application Programming Interfaces (APIs) are also provided to enable integration with desktop software such as Microsoft Office, Enterprise Resource Planning software such as SAP and Application Servers such as JBoss and IBM Websphere among many systems.
Document Management Systems, then, provide end-to-end management of the documents generated within an organisation and can also capture paper documents using scanning equipment.Sometimes the systems are distinguished from Electronic Document and Records Management Systems (EDRMS) on the basis that DMS specialises in document capture while EDRMS focus on the audit trail and security. In practice, however, Documentum and other leading DMS provide the functionality of both systems. Indeed, DMS can be viewed and are often marketed as a subset of Enterprise Content Management Systems which we will discuss in a future blog. For example, Documentum is described as an Enterprise Content Management System rather than a DMS on its corporate website while Microsoft Sharepoint (see http://sharepoint.microsoft.com) and Oracle Universal Content Management (UCM - see http://www.oracle.com/products/middleware/content-management/document-management.html) are Enterprise Content Management Systems that incorporate Document Management functionality.
The question we are now going to consider is what support can Document Management Systems provide for smart objects and indeed why would you want a DMS to present information captured from smart objects. From a technical perspective we will define smart objects as networked devices that record data and relay that data to some kind of base station. Examples include smart meters, wireless sensors, Global Positioning Satellite (GPS), Internet Protocol cameras and videos and RFID tags.
RFID systems are not only data of potential interest for a DMS but also can provide additional functionality for the DMS itself. For example, in an organisation where confidentiality and security are paramount, RFID can be used to track hard copies of documents. Other documents such as books and files can also be tracked using RFID. The RFID labels can store details of the last person to access the document, the date and time a hard copy was printed off and by whom. In conjunction with biometric technologies details of who is removing or access a document or file can be recorded. Using GPS technology the location of the document can be tracked. All of this data can be pertinent to organisations and can provide an additional layer of security within the DMS.
Wireless Sensor Networks (WSNs) provide a rich pool of data that can be used to generate documents for a DMS. In highly regulated environments such as pharmceuticals and other process manufacturing industries WSNs can generate data which could be transformed into meaningful information by Vertoda Framework. The Vertoda Framework can be integrated with a DMS to automatically generate documents based on this data. Key metrics such as temperature, humidity etc. can be recorded thus providing an audit trail for an organisation for regulatory purposes. And, of course, like RFID, sensors can be embedded in the documents themselves to control and monitor access.
The data provided by smart meters can also be captured by a DMS through Vertoda Middleware. This data is critical to utlities as it essentially determines the revenue the utility will earn. Using this information, billing documents can be generated. One of the key values of the smart grid is more accurate measurement of energy consumption. A DMS can play a key role in achieving this goal.
Smart Objects then open up an additional rich pool of data for a documents produced within a DMS. As well as measurement and detection data, multimedia data such as sound, photos and video can be captured and integrated within a DMS using Vertoda Middleware. Smart Objects provide data from a variety of diverse sources and computing environments. A typical DMS focuses on the management of documents in a fixed network corporate environment. To generate documentation from data captured in the external environment, a DMS will require the data capture and transformation provided by the Vertoda Framework. Similary, for securing documents in transit a DMS can not only leverage the features of smart objects like RFID but can also use the digital signature and cryptography services provided by Vertoda. |
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