Online Analytical Processing (OLAP)

• Suppose there is a company which has four different products – Nuts, Bolts, Washers, Screws – in the East, West, Central Regions

• If it is needed to find out how many washers were sold in each of the sales regions and compare it with the projected sales OLAP will be needed

• OLAP supports multidimensional data analysis, enabling users to view the same data in different ways using multiple dimensions.

• Each aspect of information – products, pricing, cost, region- represents a different dimensions.

• OLAP enables users to obtain online answers to ad hoc questions in a fairly rapid amount of time.

Overview of OLAP systems

• At the core of any OLAP system is an OLAP cube (also called a 'multidimensional cube' or a hypercube).

• It consists of numeric facts called measures which are categorized by dimensions. The measures are placed at the intersections of the hypercube, which is spanned by the dimensions as a vector space.

• The usual interface to manipulate an OLAP cube is a matrix interface like Pivot tables in a spreadsheet program, which performs projection operations along the dimensions, such as aggregation or averaging.

OLAP Cube Definition

• An OLAP Cube is a data structure that allows fast analysis of data according to the multiple Dimensions that define a business problem.

• A multidimensional cube for reporting sales might be, for example, composed of 7 Dimensions: Salesperson, Sales Amount, Region, Product, Region, Month, Year.

OLAP Cube Advantages

• The arrangement of data into Cubes overcomes a limitation of relational databases, which are not well suited for near instantaneous analysis and display of large amounts of data.

• Instead, they are better suited for creating records from a series of transactions known as OLTP or On-Line Transaction Processing.

• Although many report-writing tools exist for relational databases, these are slow when the whole database must be summarized, and present great difficulties when users wish to re-orient reports or analyses according to different, multidimensional perspectives, aka, Slices.

• The use of Cubes facilitate this kind of fast end-user interaction with data

• OLAP Cube can be thought of as an extension of the modeling structure provided by a spreadsheet, which accommodates data in rows and columns–i.e., a two-dimensional array of data.

• A Cube can accommodate any number of arrays, or Dimensions, though designers of OLAP Cubes will try to build models that balance user needs and logical model limitations

Operations

• Conceiving data as a cube with hierarchical dimensions leads to conceptually straightforward operations to facilitate analysis. Aligning the data content with a familiar visualization enhances analyst learning and productivity.

• The user-initiated process of navigating by calling for page displays interactively, through the specification of slices via rotations and drill down/up is sometimes called "slice and dice". Common operations include slice and dice, drill down, roll up, and pivot.

• The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database.

• Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.

• Each measure can be thought of as having a set of labels, or meta-data associated with it. A dimension is what describes these labels; it provides information about the measure.

• A simple example would be a cube that contains a store's sales as a measure, and Date/Time as a dimension. Each Sale has a Date/Time label that describes more about that sale.

• Multidimensional databases

• Multidimensional structure is defined as "a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data".

• The structure is broken into cubes and the cubes are able to store and access data within the confines of each cube. "Each cell within a multidimensional structure contains aggregated data related to elements along each of its dimensions".

• Even when data is manipulated it remains easy to access and continues to constitute a compact database format. The data still remains interrelated. Multidimensional structure is quite popular for analytical databases that use online analytical processing (OLAP) applications.

• Analytical databases use these databases because of their ability to deliver answers to complex business queries swiftly. Data can be viewed from different angles, which gives a broader perspective of a problem unlike other models.

Aggregations

• It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time required for the same query on OLTP relational data.

• The most important mechanism in OLAP which allows it to achieve such performance is the use of Aggregations.

• Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions.

• The number of possible aggregations is determined by every possible combination of dimension granularities.

• The combination of all possible aggregations and the base data contains the answers to every query which can be answered from the data.

• At the simplest form an Aggregate is a simple summary table that can be derived by performing a Group by SQL query. A more common use of aggregates is to take a dimension and change the granularity of this dimension.

• When changing the granularity of the dimension the fact table has to be partially summarized to fit the new grain of the new dimension, thus creating new dimensional and fact tables, fitting this new level of grain.

• Because usually there are many aggregations that can be calculated, often only a predetermined number are fully calculated; the remainder are solved on demand.

• The problem of deciding which aggregations (views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both.

• The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time. View selection is NP-Complete. Many approaches to the problem have been explored, including greedy algorithms, randomized search, genetic algorithms and A* search algorithm.

Datawarehouse

• Data Warehouse is a relational database that is designed for query and analysis rather than for transaction processing.

• It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.

• In addition to a Relational Database, a Data warehouse environment includes an -:

• ETL

• Extraction,

• Transportation,

• Transformation, and

• Loading solution,

• Online Analytical Processing (OLAP) Engine, Client Analysis Tools, and other applications that manage the process of gathering data and delivering it to business users.

• A common way of introducing data warehousing is to refer to the characteristics of a Data Warehouse as -:

• Subject Oriented

• Integrated

• Nonvolatile

• Time Variant

Subject Oriented

• Data warehouses are designed to help you analyze data. For example, to learn more about your company's sales data, you can build a warehouse that concentrates on sales.

• Using this warehouse, you can answer questions like "Who was our best customer for this item last year?"

• This ability to define a data warehouse by subject matter, sales in this case, makes the data war

• ehouse subject oriented.

Integrated

• Integration is closely related to subject orientation.

• Data warehouses must put data from disparate sources into a consistent format.

• They must resolve such problems as naming conflicts and inconsistencies among units of measure.

• When they achieve this, they are said to be integrated.

Nonvolatile

• Nonvolatile means that, once entered into the warehouse, data should not change.

• This is logical because the purpose of a warehouse is to enable you to analyze what has occurred.

Time Variant

• In order to discover trends in business, analysts need large amounts of data.

• This is very much in contrast to Online Transaction Processing (OLTP) systems, where performance requirements demand that historical data be moved to an archive.

• A data warehouse's focus on change over time is what is meant by the term time variant.

Differences between typical Data Warehouses and OLTP systems

• Data warehouses and OLTP systems have very different requirements. Here are some examples of differences between typical data warehouses and OLTP systems:

• Workload

• Data warehouses are designed to accommodate ad hoc queries. You might not know the workload of your data warehouse in advance, so a data warehouse should be optimized to perform well for a wide variety of possible query operations.

• OLTP systems support only predefined operations. Your applications might be specifically tuned or designed to support only these operations.

• Data modifications

• A data warehouse is updated on a regular basis by the ETL process (run nightly or weekly) using bulk data modification techniques. The end users of a data warehouse do not directly update the data warehouse.

• In OLTP systems, end users routinely issue individual data modification statements to the database. The OLTP database is always up to date, and reflects the current state of each business transaction.

• transaction.

• Schema design

• Data warehouses often use denormalized or partially denormalized schemas (such as a star schema) to optimize query performance.

• OLTP systems often use fully normalized schemas to optimize update/insert/delete performance, and to guarantee data consistency.

• Typical operations

• A typical data warehouse query scans thousands or millions of rows. For example, "Find the total sales for all customers last month."

• A typical OLTP operation accesses only a handful of records. For example, "Retrieve the current order for this customer."

• Historical data

• Data warehouses usually store many months or years of data. This is to support historical analysis.

• OLTP systems usually store data from only a few weeks or months. The OLTP system stores only historical data as needed to successfully meet the requirements of the current

Normalization

• Independent entities and relationships in the source data should not be grouped together in the same relation in the database schema.

• In particular, source specific schema elements should not be grouped with overlapping schema elements, if the grouping co-locates independent entities or relationships.

Example of two Schema Integrations

• Suppose we want a mediated (database) schema to integrate two travel databases, Go-travel and Ok-travel.

• Go-travel has two relations:

• Go-flight(f-num, time, meal(yes/no))

• Go-price(f-num, date, price)

• (f-num being the flight number)

• Ok-travel has just one relation:

• Ok-flight(f-num, date, time, price, nonstop(yes/no))

• The overlapping information in Ok-travel’s and Go-travel’s schemas could be represented in a mediated schema:

• Flight(f-num, date, time, price)

• OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF).

- OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in

• multidimensional schemas (usually star schema).

Data Warehouse Architecture

• Different data warehousing systems have different structures.

• Some may have an ODS (operational data store),

• while some may have multiple data marts.

• There are different layers of a data warehouse architecture.

In general, all data warehouse systems have the following layers:

• Data Source Layer

• Data Extraction Layer

• Staging Area

• ETL Layer

• Data Storage Layer

• Data Logic Layer

• Data Presentation Layer

• Metadata Layer

• System Operations Layer

Data Source Layer

• This represents the different data sources that feed data into the data warehouse. The data source can be of any format -- plain text file, relational database, other types of database, Excel file, etc., can all act as a data source.

• Many different types of data can be a data source:

• Operations -- such as sales data, HR data, product data, inventory data, marketing data, systems data.

• Web server logs with user browsing data.

• Internal market research data.

• Third-party data, such as census data, demographics data, or survey data.

• All these data sources together form the Data Source Layer.

• Data Extraction Layer

• Data gets pulled from the data source into the data warehouse system. There is likely some minimal data cleansing, but there is unlikely any major data transformation.

• Staging Area

• This is where data sits prior to being scrubbed and transformed into a data warehouse / data mart. Having one common area makes it easier for subsequent data processing / integration.

• Data staging The data stored to sources should be extracted, cleansed to remove inconsistencies and fill gaps, and integrated to merge heterogeneous sources into one common schema.

• The so-called Extraction, Transformation, and Loading tools (ETL) can merge heterogeneous schemata, extract, transform, cleanse, validate, filter, and load source data into a data warehouse .

• Technologically speaking, this stage deals with problems that are typical for distributed information systems, such as inconsistent data management and incompatible data structures .

• ETL Layer

• This is where data gains its "intelligence", as logic is applied to transform the data from a transactional nature to an analytical nature. This layer is also where data cleansing happens. The ETL design phase is often the most time-consuming phase in a data warehousing project, and an ETL tool is often used in this layer.

• Data Storage Layer

• This is where the transformed and cleansed data sit. Based on scope and functionality, 3 types of entities can be found here:

• data warehouse,

• data mart, and

• operational data store (ODS).

• In any given system, you may have just one of the three, two of the three, or all three types.

• Data Logic Layer

• This is where business rules are stored. Business rules stored here do not affect the underlying data transformation rules, but do affect what the report looks like.

• Data Presentation Layer

• This refers to the information that reaches the users.

• This can be in a form of a tabular / graphical report in a browser, an emailed report that gets automatically generated and sent everyday, or an alert that warns users of exceptions, among others.

• Usually an OLAP tool and/or a reporting tool is used in this layer.

• Metadata Layer

• This is where information about the data stored in the data warehouse system is stored.

• A logical data model would be an example of something that's in the metadata layer.

• A metadata tool is often used to manage metadata.

• System Operations Layer

• This layer includes information on how the data warehouse system operates, such as ETL job status, system performance, and user access history.

• A Data Mart is the access layer of the Data warehouse environment that is used to get data out to the users.

• The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team.

• Data marts are small slices of the data warehouse. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department.

• In some deployments, each department or business unit is considered the owner of its data mart including all the hardware, software and data.

• This enables each department to use, manipulate and develop their data any way they see fit;

• Without altering information inside other data marts or the data warehouse.

• In other deployments where conformed dimensions are used, this business unit ownership will not hold true for shared dimensions like customer, product, etc.

• A data mart is basically a condensed and more focused version of a data warehouse that reflects the regulations and process specifications of each business unit within an organization.

• Each data mart is dedicated to a specific business function or region. This subset of data may span across many or all of an enterprise’s functional subject areas.

• It is common for multiple data marts to be used in order to serve the needs of each individual business unit (different data marts can be used to obtain specific information for various enterprise departments, such as accounting, marketing, sales, etc.).

• Data mart vs Data warehouse:

• Holds multiple subject areas

• Holds very detailed information

• Works to integrate all data sources

• Does not necessarily use a dimensional model

• Data mart:

• Often holds only one subject area- for example, Finance, or Sales

• May hold more summarized data (although many hold full detail)

• Concentrates on integrating information from a given subject area or set of source systems

• Is built focused on a dimensional model using a star schema.

data warehouse

Reasons for creating a Data Mart

• Easy access to frequently needed data

• Creates collective view by a group of users

• Improves end-user response time

• Ease of creation

• Lower cost than implementing a full data warehouse

• Potential users are more clearly defined than in a full data warehouse

• Contains only business essential data and is less cluttered.

Design schemas

• Star schema - fairly popular design choice; enables a relational database to emulate the analytical functionality of amultidimensional database

• Snowflake schema

Decision Support System

• DSS are a natural progression from information reporting systems and transaction processing systems.

• DSS are interactive, computer-based information systems that use decision models and specialized databases to assist the Decision-Making processes of Managerial End users.

• They provide Managerial End users with information in an interactive session on ad hoc basis.

• DSS provides managers with analytical modeling, simulation, data retrieval and information presentation capabilities.

• Managers generate the information they need for more unstructured types decisions in an interactive , simulation-based process.

• For Eg electronic spreadsheets allow a Manager to pose a series of what-if questions and receive interactive responses to such ad hoc requests for information.

Decision Support System

• A Decision Support System (DSS) is a computer-based Information System that supports business or organizational decision-making activities.

• DSSs serve the management, operations, and planning levels of an organization (usually mid and higher management)

• It helps to make decisions, which may be rapidly changing and not easily specified in advance (Unstructured and Semi-Structured decision problems).

• Decision support systems can be either fully computerized, human or a combination of both.

DSS by its characteristics

• DSS tends to be aimed at the less well structured, underspecified problem that upper level managers typically face;

• DSS attempts to combine the use of models or analytic techniques with traditional data access and retrieval functions;

• DSS specifically focuses on features which make them easy to use by non-computer people in an interactive mode; and

• DSS emphasizes flexibility and adaptability to accommodate changes in the environment and the decision-making approach of the user.

• DSSs include knowledge-based systems. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, and personal knowledge, or business models to identify and solve problems and make decisions.

• Typical information that a decision support application might gather and present includes:

• Inventories of information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),

• Comparative sales figures between one period and the next,

• Projected revenue figures based on product sales assumptions.

Components

• Design of Decision Support System

• Three fundamental components of a DSS architecture are:

1. The database (or knowledge base),

2. The model (i.e., the decision context and user criteria), and

3. The user interface.

4. The users themselves

Development frameworks

• DSS systems are not entirely different from other systems and require a structured approach. Such a framework includes people, technology, and the development approach.

• The Early Framework of Decision Support System consists of four phases:

1. Intelligence Searching for conditions that call for decision.

2. Design Developing and analyzing possible alternative actions of solution.

3. Choice Selecting a course of action among those.

4. Implementation Adopting the selected course of action in decision situation.

DSS Technology Levels (of hardware and software) may include:

• This is the part of the application that allows the decision maker to make decisions in a particular problem area. The user can act upon that particular problem.

• Generator contains Hardware/software environment that allows people to easily develop specific DSS applications. This level makes use of case tools or systems such as Crystal, Analytica and iThink.

• Tools include lower level hardware/software. DSS generators including special languages, function libraries and linking modules

• An iterative developmental approach allows for the DSS to be changed and redesigned at various intervals. Once the system is designed, it will need to be tested and revised where necessary for the desired outcome.

Classification

• There are several ways to classify DSS applications. Not every DSS fits neatly into one of the categories, but may be a mix of two or more architectures.

• DSS is classified into the following six frameworks:

1. text-oriented DSS,

2. database-oriented DSS,

3. spreadsheet-oriented DSS,

4. solver-oriented DSS,

5. rule-oriented DSS,

6. compound DSS.

• A compound DSS is the most popular classification for a DSS. It is a hybrid system that includes two or more of the five basic structures described

• The support given by DSS can be separated into three distinct, interrelated categories:

• Personal Support,

• Group Support, and

• Organizational Support.

DSS components may be classified as:

• Inputs: Factors, numbers, and characteristics to analyze

• User Knowledge and Expertise: Inputs requiring manual analysis by the user

• Outputs: Transformed data from which DSS "decisions" are generated

• Decisions: Results generated by the DSS based on user criteria

• DSSs which perform selected cognitive decision-making functions and are based on artificial intelligence or intelligent agents technologies are called Intelligent Decision Support Systems (IDSS)

• The nascent field of Decision engineering treats the decision itself as an engineered object, and applies engineering principles such as Design and Quality assurance to an explicit representation of the elements that make up a decision.

Group Decision Support System

Time/Place Framework

• Same Time/Same Place

– decision room

• Same Time/Different Place

– telephone conferencing, video conferencing

• Different Time/Same Place

– project/team rooms, shared offices

• Different Time/Different Place

– email, workflow management systems

Group Decision Support Systems (GDSS)

• Group Support Systems (GSS)

• Electronic Meeting Systems

• Collaborative Computing

• Evolved as information technology researchers recognized that technology could be developed for supporting meeting activities

– Idea generation

– Consensus building

– Anonymous ranking

– Voting, etc.

Important Characteristicsof a GDSS

• Specially Designed Information System

• Goal of Supporting Groups of Decision Makers

• Easy to Learn and Use

• May be designed for one type of problem or for many organizational decisions

• Designed to encourage group activities

• Attempts to minimize process losses

Three Levels of GDSS Support

• Based on DeSanctis and Gallupe

– Level 1: Process Support

– Level 2: Decision-making Support

– Level 3: Rules of order

Level 1: Process Support

• Supports the basic communication process between participants

– electronic messaging

– network linking the PCs

– public screen

– anonymous input of votes and ideas

– solicitation of ideas or votes

– summary and display of ideas and opinions

– format for an agenda

Level 2: Decision-Making Support

• Decision Modeling and Group Decision Techniques aimed at reducing Uncertainty and that occur in the group decision process

• adds capabilities for modeling and decision analysis

– planning and financial models

– decision trees

– probability assessment models

– resource allocation models

Level 3: Rules of Order

• Rule of order ensures that the group involved in the group meeting can conduct it’s business in a way that is both fair and effective.

• Characterized by machine-induced group communication patterns

• Control the pattern, timing, or content of information exchange

• Special software containing rules of order is added

– rules determining the sequence of speaking, the appropriate response, or voting rules

Groupware Technologies

• Groupware is defined as any software that enables group collaboration over a network.

• These technologies have the potential to increase collaboration at a distance while reducing the cost of travel and the time knowledge workers waste in transit.

Groupware provides

• flexible communication structures (connecting people in new ways),

• increased communication speed,

• increased work performance and productivity,

• organizational memory capability, etc.

Examples of Groupware Technologies include:

• Shared authoring tools such as MS Office applications (Word, Excel, etc.) which include common word processing programs, graphics programs and sound-editing facilities. Many stand-alone applications can be considered as groupware if they can access and modify a document on the web or a common server

• E-mail systems such as MS Outlook Express, support multiple text-based communications and is the most often used groupware Online forums are real-time, text-based systems that allow group posting and response to text messages. They are self-archiving, in that the sequence of text-based conversations involving dozens or even hundreds of contributors is maintained for review by others

• Instant messaging such as AOL messenger, is a growing form of groupware that allows knowledge workers working away from their desks to exchange short items of information

• Screen sharing allows a user with the appropriate access privileges to connect to and take control of a remote PC. It is popular in training and troubleshooting situations where a support person can show the trainee at a remote site how to perform an operation and then watch as the trainee attempts to do the operation

• Electronic whiteboard provides a virtual whiteboard drawing space that enables multiple collaborators to take turns at authoring and modifying hand-drawn graphics or simply by posting a slide for a presentation. They are used in conjunction with other products, such as videoconferencing which is the real-time, multi-way broadcasting of video and audio

• Videoconferencing such as Skype conferences, allow real-time, multi-way broadcasting of video and audio, using telephone lines for audio and the Internet or other networks for the video channels

• Multimodal conferencing supports real-time group sharing of an electronic whiteboard, a text forum, audio, and multiple-channel video and audio.

What is “Groupware?”

• Tools (hardware, software, processes) that support person-to-person collaboration

• This can include e-mail, bulletin boards, conferencing systems, decision support systems, video and workflow systems, etc…

• Some common groupware acronyms:

– Group Support Systems (GSS)

– Group Decision Support Systems (GDSS)

– Electronic Meeting Systems (EMS)

– Bulletin Board Systems (BBS)

– Group Collaboration Systems (GCS)

– Computer-Supported Cooperative Work (CSCW) systems

Groupware and Levels of Collaboration

• Groupware can be divided into three categories depending on the level of collaboration:

1. Communication can be thought of as unstructured interchange of information. A phone call or an IM Chat discussion are examples of this.

2. Conferencing (or collaboration level, as it is called in the academic papers that discuss these levels) refers to interactive work toward a shared goal. Brainstorming or Voting are examples of this.

3. Co-ordination refers to complex interdependent work toward a shared goal. A good metaphor for understanding this is to think about a sports team; everyone has to contribute the right play at the right time as well as adjust their play to the unfolding situation - but everyone is doing something different - in order for the team to win. That is complex interdependent work toward a shared goal: collaborative management.

Electronic Communication Tools

• Electronic communication tools send messages, files, data, or documents between people and hence facilitate the sharing of information. Examples include:

• Synchronous conferencing

• Asynchronous conferencing

• E-mail

• Faxing

• Voice mail

• Wikis

• Web publishing

• Revision control

Electronic Conferencing is Tools

• Electronic conferencing tools facilitate the sharing of information, but in a more interactive way. Examples include:

• Internet forums (also known as message boards or discussion boards) — a virtual discussion platform to facilitate and manage online text messages

• Online chat— a virtual discussion platform to facilitate and manage real-time text messages

• Instant Messaging

• Telephony — telephones allow users to interact

• Videoconferencing — networked PCs share video and audio signals

• Data conferencing — networked PCs share a common whiteboard that each user can modify

• Application sharing — users can access a shared document or application from their respective computers simultaneously in real time

• Electronic meeting systems (EMS) — originally these were described as "electronic meeting systems," and they were built into meeting rooms. These special purpose rooms usually contained video projectors interlinked with numerous PCs; however, electronic meeting systems have evolved into web-based, any time, any place systems that will accommodate "distributed" meeting participants who may be dispersed in several locations.

Collaborative Management (coordination) Tools

• Collaborative management tools facilitate and manage group activities. Examples include:

• Electronic calendars (also called time management software) — schedule events and automatically notify and remind group members

• Project management systems — schedule, track, and chart the steps in a project as it is being completed

• Online proofing — share, review, approve, and reject web proofs, artwork, photos, or videos between designers, customers, and clients

• Workflow systems — collaborative management of tasks and documents within a knowledge-based business process

• Knowledge Management Systems— collect, organize, manage, and share various forms of information

• Enterprise Bookmarking — collaborative bookmarking engine to tag, organize, share, and search enterprise data

• Prediction Markets — let a group of people predict together the outcome of future events

• Extranet Systems (sometimes also known as 'project extranets') — collect, organize, manage and share information associated with the delivery of a project (e.g.: the construction of a building)

• Social Software Systems — organize social relations of groups

• Online Spreadsheets— collaborate and share structured data and information

• Client Portals — interact and share information with your clients in a private online environment

Benefits of GDSS

• supports parallel generation of ideas

• supports larger groups

• rapid and easy access to external information

• parallel computer discussion

• anonymous input

• automatic documentation of the group meetings

Groupware(Collaborative software)

• Collaboration, with respect to information technology, seems to have several definitions. Some are defensible but others are so broad they lose any meaningful application.

• Understanding the differences in human interactions is necessary to ensure the appropriate technologies are employed to meet interaction needs.

Collaborative Software

• Collaborative software helps facilitate the action-oriented team working together over geographic distances by providing tools that help communication, collaboration and the process of problem solving by providing the team with a common means for communicating ideas and brainstorming.

• Additionally, collaborative software may support project management functions, such as task assignments, time-management with deadlines and shared calendars.

• The artifacts, the tangible evidence of the problem solving process, including the final outcome of the collaborative effort, typically require documentation and archiving of the process itself, and may involve archiving project plans, deadlines and deliverables.

The primary ways in which humans interact in an organization

• Conversational interaction is an exchange of information between two or more participants where the primary purpose of the interaction is discovery or relationship building. There is no central entity around which the interaction revolves but is a free exchange of information with no defined constraints generally focused on personal experiences. Communication technology such as telephones, instant messaging, and e-mail are generally sufficient for conversational interactions.

• Transactional interaction involves the exchange of transaction entities where a major function of the transaction entity is to alter the relationship between participants. The transaction entity is in a relatively stable form and constrains or defines the new relationship. One participant exchanges money for goods and becomes a customer. Transactional interactions are most effectively handled by transactional systems that manage state and commit records for persistent storage.

• In Collaborative Interactions the main function of the participants' relationship is to alter a collaboration entity (i.e., the converse of transactional). The collaboration entity is in a relatively unstable form.

• Examples include the development of an idea, the creation of a design, the achievement of a shared goal. Therefore, real collaboration technologies deliver the functionality for many participants to augment a common deliverable.

• Record or document management, threaded discussions, audit history, and other mechanisms designed to capture the efforts of many into a managed content environment are typical of collaboration technologies.

By method used we can divide Collaborative Software into

• Web-based collaborative tools

• Software collaborative tools

By area served we can divide collaborative software into:

• Knowledge management tools

• Knowledge creation tools

• Information sharing tools

• Collaborative project management tools

Collaborative Project Management Tools

• Collaborative project management tools (CPMT) are very similar to collaborative management tools (CMT) except that CMT may only facilitate and manage a certain group activities for a part of a bigger project or task, while CPMT covers all detailed aspects of collaboration activities and management of the overall project and its related knowledge areas.

• Another major difference is that CMT may include social software, Document Management System (DMS) and Unified Communication (UC) while CPMT mostly considers business or corporate related goals with some kind of social boundaries most commonly used for project management.

• CPMT facilitate and manage social or group project based activities.

• Examples include:

• Electronic calendars

• Project management systems

• Resource Management

• Workflow systems

• Knowledge management

• Prediction markets

• Extranet systems

• Social software

• Online spreadsheets

• Online artwork proofing, feedback, review and approval tool

• In addition to most CPMT examples, CMT also includes:

• HR and equipment management

• Time and cost management

• Online chat

• Instant messaging

• Telephony

• Videoconferencing

• Web conferencing

• Data conferencing

• Application sharing

• Electronic Meeting Systems (EMS)

• Synchronous conferencing

• E-mail

• Faxing

• voice mail

• Wikis

• Web publishing

• Revision control

• Charting

• Document-centric collaboration

• Document retention

• Document sharing

• Document repository

• Evaluation and survey

Group Decision Making

• Many of the decisions in today's workplace are made by groups of individuals

• Groups bring many advantages to the choice process:

– Multiple source of knowledge and experience

– A wider variety of prospectives

– Potential synergy associated with collaborative activity

• Some times too many decision makers result in either a bad decision or no decision at all.

• Group in term of decision making can be defined as : a collective entity that is independent of the properties of its members.

• Multiparticipant decision maker (MDM ): An activity conducted by a collective entity composed of two or more individuals and characterised in terms of both the properties of the collective entity and of its individual members

Classification of Multi-participant Decision -Making structures

• Decision structure, two types:

– Collaborative

• Group decision structure: Formal participants and multiple decision maker

– Negotiation decisions

– Majority decisions

– Noncollaborative

• Team decision structure: Formal participants and single decision maker

– Negotiation decisions

– Majority decisions

• Individual decision structure

Communication Networks

• The structure of an MDM is primarily based on the interaction and flow of communication among the various members.

• Communication can be thought as any means by which information is transmitted to one or more members of the MDM.

• Basic Types of Networks Structures

1. Wheel Network

2. Chain Network

3. Circle Network

4. Completely Connected Network

Classification of networks according to centrality

• Highly Centralised

– They are efficient to routine and recurring decisions.

– They tend to strengthen the leadership position of the central members.

– They tend to result in a stable set of interactions among the participants.

– They tend to produce lower average levels of satisfaction among the participants.

– Highly Decentralised

– They tend to produce higher average levels of satisfaction among participants.

– They facilitate nonroutine or nonrecurring decisions.

– They promote innovation and creative solutions.

Factors used in determining Decision Structure

1. The importance of the quality of the decision.

2. The extent to which the decision maker possess the knowledge and expertise to make the decision.

3. The extent to which potential participants have the necessary information.

4. The degree of structuredness of the problem context.

5. The degree to which the acceptance or commitment is critical to successful implementation.

6. The probability of acceptance of an autocratic decision.

7. The degree of motivation among the participants to achieve the organisational goals.

8. The degree of potential conflicts among the participants over a preferred solution.

Problems with Group Decisions

1. Size

– The most widely studied and consequential component of group decision making.

– Studies show that as the size of a group increases, individual satisfaction tends to decrease.

As the size increases, the less active members tend to become noticeably less productive.

– Logic suggests that the management of an MDM requiring consensus or majority is easier when the size is small.

Problems with Group Decisions: Size..

– Member cohesiveness decreases as MDM size increases. When membership is high, subgroups and internal coalitions tend to form that serve redirect the focus of the participant away from the common goal.

– The increased likelihood for certain members of large MDMs to feel threatened reluctant to participate because the size magnifies the impersonal nature of the problem context.

– Despite the disadvantages when the size of the MDM increases, in certain situations such as quantitative judgment in statistics, the larger the membership of the MDM, the more likley it is that the results of the judgment must be made.

Effects related to MDM (Management DM) size

– Participant interaction tends to decrease as size increase.

– Affective or emotional relationships tend to decrease as size increases.

– Central, dominant leadership tend to increase as size increases.

– Conflicts is resolved with political rather than analytical solutions as size increases.

– Despite the disadvantages when the size of the MDM increases, in certain situations such as quantitative judgment in statistics, the larger the membership of the MDM, the more likely it is that the results of the judgment must be made.

Problems with Group Decisions

2. Groupthink: a mode of thinking that people engage in when they are deeply involved in a cohesive in-group.

– The more friendly and cooperative the members of a group, the greater the likelihood that independent critical thinking will be suspended in deference to group norms.

– Unfavourable outcomes associated with Groupthink

– Tends to prevent a complete open-mind analysis of opportunities in the development of objectives.

– Holds back a meaningful search for information and tends to bias any searches toward a self fulfilling selectivity.

– Limits the participant’s ability to appraise possibilities associated with the cost of failure.

– Tends to eliminate the formation of incident of fallback position.

3. Other Social issues

– Conflict

• The desire to be viewed as a “good” member and to be accepted by the other participants often leads to conflict avoidance.

• Natural group dynamics such as struggle of power can result in some form of conflict.

– Anonymity

• One common method used to control sources of potential conflict and to support other MDM processes is participant anonymity, i.e. vote.

• In many cases anonymity results in the generation of more and better information.

MDM Support Technologies

– Tools used in MDM environment to support the processes and activities related to the decision making process.

– Usual group meeting description ….. (Gray 1981).

– New technologies and telecommunications ……

– MDM support technologies can be classified based on decision maker styles .

The four basic levels of MDM technology:

1. Organisational Decision Support System ( ODSS ): A complex system of computer based technologies- including those that facilitate communication- that provides support for decision makers.

2. Group Support Systems ( GSS ): A collective of computer based technologies used to aid MDM in identifying and addressing problems, opportunities, and issues.

3. Group Decision support System ( GDSS ): A collective of computer based technologies designed to support the activities and processes related to MDM.

4. Decision Support System ( DSS ): a computer program under the control of one or more persons that provides staff within organisations with support tools capable of enhancing the results of the decision making process.

Gains and Losses Associated with MDM Activities

Some of the Gain

1. Collective has greater knowledge than a single participant.

2. Allows for synergistic results.

3. Interaction stimulates the generation of knowledge.

4. Participants can improve individual performance through learning from others.

Some of the Losses

1. Can block the production of ideas.

2. Can produce information overload much faster.

3. Relative collection of speaking time is reduced with MDM size

4. Increase opportunities of socialising over goal focus.

Types by features offered in support of the multi-participant decision-making activities:

1. Reduce communication barriers.

2. Reduce uncertainty and noise.

3. Organize decision process.

Types by technology used:

1. Electronic boardroom.

2. Teleconference room.

3. Group network.

4. Information centre .

5. Collaboration laboratory.

6. Decision room.

Collaborative Support Technologies

Groupware: A particular type of MDM support technology specifically focused on issues related to collaborative processes among people. You can think of it as a tool that, when deployed and used appropriately, positively affects that way people communicate with each other, resulting in an improvement in the way people work.

Current market leaders of Groupware:

– Lotus Notes

– Microsoft Exchange

– Oracle Office

– GroupWise

– Team Office

– Groupware refers to programs that help people work together collectively while located remotely from each other. Programs that enable real time collaboration are called synchronous groupware.

– Groupware services can include the sharing of calendars, collective writing, e-mail handling, shared database access, electronic meetings with each person able to see and display information to others, and other activities.

– Sometimes called collaborative software, groupware is an integral component of a field of study known as Computer-Supported Cooperative Work or CSCW.

– Groupware is often broken down into categories describing whether or not work group members collaborate in real time (synchronous groupware and asynchronous groupware).

– Some product examples of groupware include Lotus Notes and Microsoft Exchange, both of which facilitate calendar sharing, e-mail handling, and the replication of files across a distributed system so that all users can view the same information.

– Electronic "face-to-face" meetings are facilitated by CU-See Me and Microsoft NetMeeting.

Five Basic group processes

Dynamic Group Interaction model

Basic Principles

• The effectiveness of a group can be expressed in terms of three types of outcomes, i.e. (quality and quantity of the )products, individual ‘rewards’ and vitality of the social relations.

• Effectiveness depends on the quality of the individual preformance and six group processes, which have to match

• The quality of the group processes depends on the support of six conditions, and on the interaction with the environment.

• The six aspects of the context-of-use have to fit to each other.

• Groups develop and tools become adopted and adapted to, through interaction processes and feedback.

SUPPORT – MATCH – ADAPTATION

Lessons learned (1)

1. Groupware is part of a social system. Design not for a tool as such but for a new socio-technical setting.

2. Design for several levels of interaction, i.e. for user friendly human computer interaction, adequate interpersonal communication, group co-operation and organisational functioning.

3. Design in a participative way, i.e. users and possibly other stakeholders should be part of the design process from the beginning.

4. Analyse carefully the situation of the users. Success of collaboration technology depends on the use and the users, not on the technology. Introduction should match their skills and abilities, and also their attitudes, otherwise resistance is inevitable.

5. Analyse carefully the context, since success of collaboration technology depends on the fit to that context. The more a new setting deviates from the existing one the more time, energy and other resources should be mobilised to make it a success.

Lessons learned (2)

6. Introduce the new system carefully. Apply proper project management, find a champion, try a pilot, inform people intensively

7. Train and support end-users extensively

8. Measure success conditions and success criteria before, during and after the development process. Only in this way you can learn for future developments.

9. Plan for a long process of introduction, incorporation, evaluation and adaptation. Groupware is not a quick fix.

10. Despite careful preparations groupware is appropriated and adapted in unforeseen ways. Keep options open for new ways of working with the groupware, because this may result in creative and innovative processes.

6. Introduce the new system carefully. Apply proper project management, find a champion, try a pilot, inform people intensively

7. Train and support end-users extensively

8. Measure success conditions and success criteria before, during and after the development process. Only in this way you can learn for future developments.

9. Plan for a long process of introduction, incorporation, evaluation and adaptation. Groupware is not a quick fix.

10. Despite careful preparations groupware is appropriated and adapted in unforeseen ways. Keep options open for new ways of working with the groupware, because this may result in creative and innovative processes.

Expert Systems

• Expert Systems are computer programs that are derived from a branch of computer science research called Artificial Intelligence (AI).

• AI's scientific goal is to understand intelligence by building computer programs that exhibit intelligent behavior.

• It is concerned with the concepts and methods of symbolic inference, or reasoning, by a computer, and how the knowledge used to make those inferences will be represented inside the machine.

• AI programs that achieve expert-level competence in solving problems in task areas by bringing to bear a body of knowledge about specific tasks are called knowledge-based or expert systems.

• Often, the term expert systems is reserved for programs whose knowledge base contains the knowledge used by human experts, in contrast to knowledge gathered from textbooks or non-experts.

• More often than not, the two terms, expert systems (ES) and knowledge-based systems (KBS), are used synonymously. Taken together, they represent the most widespread type of AI application. The area of human intellectual endeavor to be captured in an expert system is called the task domain.

• Task refers to some goal-oriented, problem-solving activity. Domain refers to the area within which the task is being performed. Typical tasks are diagnosis, planning, scheduling, configuration and design. An example of a task domain is aircraft crew scheduling,

The Building Blocks of Expert Systems

• Every expert system consists of two principal parts: the knowledge base; and the reasoning, or inference, engine.

• The knowledge base of expert systems contains both factual and heuristic knowledge. Factual knowledge is that knowledge of the task domain that is widely shared, typically found in textbooks or journals, and commonly agreed upon by those knowledgeable in the particular field.

• Heuristic knowledge is the less rigorous, more experiential, more judgmental knowledge of performance. In contrast to factual knowledge, heuristic knowledge is rarely discussed, and

is largely individualistic. It is the knowledge of good practice, good judgment, and plausible reasoning in the field. It is the knowledge that underlies the "art of good guessing."

• Knowledge representation formalizes and organizes the knowledge. One widely used representation is the production rule, or simply rule.

• A rule consists of an IF part and a THEN part (also called a condition and an action).

• The IF part lists a set of conditions in some logical combination.

• The piece of knowledge represented by the production rule is relevant to the line of reasoning being developed if the IF part of the rule is satisfied; consequently, the THEN part can be concluded, or its problem-solving action taken.

• Expert systems whose knowledge is represented in rule form are called rule-based systems.

• Another widely used representation, called the unit (also known as frame, schema, or list structure) is based upon a more passive view of knowledge.

• The unit is an assemblage of associated symbolic knowledge about an entity to be represented. Typically, a unit consists of a list of properties of the entity and associated values for those properties.

• Since every task domain consists of many entities that stand in various relations, the properties can also be used to specify relations, and the values of these properties are the names of other units that are linked according to the relations.

• One unit can also represent knowledge that is a "special case" of another unit, or some units can be "parts of" another unit.

• The problem-solving model, or paradigm, organizes and controls the steps taken to solve the problem.

• One common but powerful paradigm involves chaining of IF-THEN rules to form a line of reasoning.

• If the chaining starts from a set of conditions and moves toward some conclusion, the method is called forward chaining.

• If the conclusion is known (for example, a goal to be achieved) but the path to that conclusion is not known, then reasoning backwards is called for, and the method is backward chaining.

• These problem-solving methods are built into program modules called inference engines or inference procedures that manipulate and use knowledge in the knowledge base to form a line of reasoning.

• In artificial intelligence, an Expert system is a computer system that emulates the decision-making ability of a human expert.

• Expert systems are designed to solve complex problems by reasoning about knowledge, represented primarily as if–then rules rather than through conventional procedural code.

• The first expert systems were created in the 1970s and then proliferated in the 1980s.

• Expert systems were among the first truly successful forms of AI software.

An Expert System is divided into two sub-systems:

• The Inference Engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging capabilities.

• The Knowledge Base which represents facts and rules.

Components of an Expert System

• As Expert Systems evolved, many new techniques were incorporated into various types of Inference Engines. Some of the most important of these were:

1. Truth Maintenance. Truth maintenance systems record the dependencies in a knowledge-base so that when facts are altered dependent knowledge can be altered accordingly. For example, if the system learns that Socrates is no longer known to be living, it will revoke the assertion that Socrates is mortal.

2. Hypothetical Reasoning. In hypothetical reasoning, the Knowledge Base can be divided up into many possible views, aka worlds. This allows the Inference Engine to explore multiple possibilities in parallel. In this simple example, the system may want to explore the consequences of both assertions, what will be true if Socrates is living and what will be true if he is not?

3. Fuzzy Logic. One of the first extensions of simply using rules to represent knowledge was also to associate a probability with each rule. So, not to assert that Socrates is mortal but to assert Socrates may be mortal with some probability value. Simple probabilities were

Expert System

extended in some systems with sophisticated mechanisms for uncertain reasoning and combination of probabilities.

4. Ontology Classification. With the addition of Object classes to the Knowledge Base a new type of reasoning was possible. Rather than reason simply about the values of the Objects, the system could also reason about the structure of the objects as well. In this simple example Man can represent an Object Class and R1 can be redefined as a rule that defines the class of all men.

5. These types of special purpose Inference Engines are known as Classifiers. Although they were not highly used in Expert systems, Classifiers are very powerful for unstructured volatile domains and are a key technology for the Internet and the emerging Semantic Web.