OLAP

OLAP (online analytical processing) is computer processing that enables a user to easily and selectively extract and view data from different points of view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company’s beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time period) as a separate “dimension.” OLAP software can locate the intersection of dimensions (all products sold in the Eastern region above a certain price during a certain time period) and display them. Attributes such as time periods can be broken down into sub attributes.

OLAP can be used for data mining or the discovery of previously undiscerned relationships between data items. An OLAP database does not need to be as large as a data warehouse, since not all transactional data is needed for trend analysis. Using Open Database Connectivity (ODBC), data can be imported from existing relational databases to create a multidimensional database for OLAP.

Two leading OLAP products are Hyperion Solution’s Essbase and Oracle’s Express Server. OLAP products are typically designed for multiple-user environments, with the cost of the software based on the number of users.

OLAP OPERATIONS

OLAP provides a user-friendly environment for interactive data analysis. A number of OLAP data cube operations exist to materialize different views of data, allowing interactive querying and analysis of the data.

The most popular end user operations on dimensional data are:

 

 

Roll up

 

The roll-up operation (also called drill-up or aggregation operation) performs aggregation on a data cube, either by climbing up a concept hierarchy for a dimension or by climbing down a concept hierarchy, i.e. dimension reduction.

Roll Down

 

The roll down operation (also called drill down) is the reverse of roll up. It navigates from less detailed data to more detailed data. It can be realized by either stepping down a concept hierarchy for a dimension or introducing additional dimensions.

Slicing

 

Slice performs a selection on one dimension of the given cube, thus resulting in a subcube.

Dicing

 

The dice operation defines a subcube by performing a selection on two or more dimensions.

Pivot

 

Pivot otheriwise known as Rotate changes the dimensional orientation of the cube, i.e. rotates the data axes to view the data from different perspectives. Pivot groups data with different dimensions.

Other OLAP operations

 

Some more OLAP operations include:

SCOPING: Restricting the view of database objects to a specified subset is called scoping. Scoping will allow users to recieve and update some data values they wish to recieve and update.

SCREENING: Screening is performed against the data or members of a dimension in order to restrict the set of data retrieved.

DRILL ACROSS: Accesses more than one fact table that is linked by common dimensions. COmbiens cubes that share one or more dimensions.

DRILL THROUGH: Drill down to the bottom level of a data cube down to its back end relational tables.

 

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Social Media Mining

SOCIAL MEDIA MINING

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The growth of social media has entirely changed the way people interact with each other. Individuals produce massive amount of data by interacting, sharing and consuming content through social media.

Social media mining integrates the social media, social media analysis and data mining to provide a convenient platform for students, researchers and project managers to understand the basics of social media mining. In simple, social media mining is extracting data from the social media. The primary objective is to handle large scale data and to get an insight of it.

CLASSIFICATION OF SOCIALMEDIA

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  • Online social networking
  • Blogging
  • Micro-blogging
  • Wikis
  • Social news
  • Media sharing
  • Opinions, reviews and bookmarking

NEED FOR SOCIAL MEDIA MINING

The social media mining can help researchers to understand the new phenomena’s to provide better services and to develop innovative opportunities. It is a growing multidisciplinary area where researchers from different background can make important contributions that matter for social media research and development.

TOOLS USED FOR SOCIAL MEDIA MINING

  • Data mining
  • Text mining
  • Web curator tool
  • Twitter tool
  • Google flip tool

USE OF DATA MINING IN SOCIAL MEDIA

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Social media data is everywhere. There is an overload of both information and interaction. Using data mining researchers can dig into data from this chaos. It helps them to directly study the opinions and behaviours of millions of users to gain insight into human behaviour, market analysis and product sentiments.

CHALLENGES IN SOCIAL MEDIA MINING

Social media data are vast, Noisy, distributed, unstructured and dynamic. These characteristics pose challenges to data mining tasks to invent new efficient techniques and algorithms.

Mobile Business Intelligence

MOBILE BUSINESS INTELLIGENCE

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Mobile BI is a software that works for more than just a desktop, it reaches to the mobile phones. These applications optimizes the traditional BI so that they can be viewed easily even in small screens and allows simple charts, graphs and Sparkline’s. One of the biggest advantage in using MBI is that it offers current status so that the workers are always offered to make real time decisions.

In simple, Mobile BI is deploying on to mobile devices such as mobile phones, smart phones and computers which is charts and dashboards will be deployed onto mobile devices.

TRADITIONAL VS MOBILE BI

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The traditional BI has been put into use for a very long time while Mobile BI is the emerging trend in BI. The traditional BI renders only in browsers but the Mobile BI renders on browsers optimized for mobile devices. The traditional BI is portable if used in a laptop but it is not handy while the Mobile BI is portable and is very easy to carry. The traditional BI is used for people who sit in a place and analyse and for senior staff when he has to make any critical decisions while the Mobile BI is used even by the store level manager.

NEED FOR MOBILE BI

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  • Mobility of executives
  • Client requirements
  • Technology
  • Gadgets

 

TYPES OF BI

  • Browser based apps

No need of network for each platform

  • Proprietary apps

Faster performance and inbuilt locations

  • Client apps

Personalised for clients

ADVANTAGES OF Mobile BI

Mobile BI Benefits

SIMPLIFIED ANALYTICS IN MOBILE

The size and shape of the phones and tablets by itself lends to simplified analytics. It does not bug us with so much of information until and unless we require more information. The visual representation makes it more colourful and interesting to read and learn from.

INCREASED COLLABORATION

The Mobile Business Intelligence platforms are all cloud based which means that we can access from anywhere and from nay device. There is no more difference with the people having computers or laptops. The companies are trying to push their employees to make use of this platform as they will be able to work smarter and report then and there. It translates to increase in collaboration.

ACCESS FOR ALL

Collaboration means bringing more people together for a mix. Mobile Business Intelligence allows us to reach to people for both temporarily and permanently from the high up people to the front line. By providing more access to people it reduces our time as each and every person has their own set of jobs to do in hand.

CHALLENGES

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SMALLER VIEWING SPACE

Organisations must work on what to display as the users must readily be able to access instead of resizing the screen or zooming it.

LIMITS ON DATA INTERACTION

Most devices limit the amount of slicing and dicing that the users use to perform on cached or stored prototypes.

SECURITY ON AUTHENTICATION

Security is of a paramount concern. Organisations must evaluate how different levels of security, authentication and access controls work in order to keep the data safe.