Question 1 - What is operational intelligence?

Ans: Operational Intelligence (OI) is a form of real-time dynamic, business analytics that delivers visibility and insight into business operations. OI solutions run query analysis against live feeds and event data to deliver real-time visibility and insight into business and IT operations. This real-time information can be acted upon in a variety of ways: alerts can be sent; business processes can be triggered and executive decisions can be made and implemented using live dashboards. More often than not, Operational Intelligence is chosen for its real-time monitoring capabilities when organizations want to take immediate action.

The purpose of OI is to monitor business activities and identify and detect situations relating to inefficiencies, opportunities, and threats and provide operational solutions. Some definitions define operational intelligence an event-centric approach to delivering information that empowers people to make better decisions.

OI helps quantify:

The efficiency of the business activities How the IT infrastructure and unexpected events affect the business

activities (resource bottlenecks, system failures, events external to the company, etc.)

How the execution of the business activities contribute to revenue gains or losses.

Question 2 - What is Business Intelligence? Explain the components of BI architecture.

Ans: - Operational business intelligence, sometimes called real-time business intelligence, is an approach to data analysis that enables decisions based on the real-time data companies generate and use on a day-to-day basis. Typically, the data is queried from within an organization’s enterprise applications. Operational business intelligence technology is primarily targeted at front-line workers, such as call centre operators, who need timely data to do their jobs.

With operational business intelligence, analysis can take place in tandem with business processing, so that problems can be spotted and dealt with sooner than with conventional after-the-fact business intelligence (BI) approaches. It enables the creation of a performance and feedback loop in which decision makers can analyse what’s happening in the business, act upon their findings and immediately see the results of those actions.

Every business intelligence (BI) deployment has an underlying architecture. The BI architecture is much like the engine of a car – a necessary component, often powerful, but one that users, like drivers, don’t always understand. For some companies new to business intelligence, the BI architecture may primarily be the operational systems and the BI front-end tools. For more mature BI deployments and particularly for enterprise customers, it will involve ETL (extract, transform, and load) tools, a data warehouse, data marts, BI front-end tools, and other such components.

There are following components: 

Operational and Source Systems: Operational systems are the starting point for most quantitative data in a company. Operational systems may also be referred to as “transaction processing systems,” “source systems,” and “enterprise resource planning” (ERP) systems. 

Manufacturing system: When a product is produced, the production order is entered in the manufacturing system. The quantity of raw material used and the finished product produced are recorded.

Sales system: When a customer places an order, the order details are entered in an order entry system.

Supply chain system: When the product is available, the product is shipped and order fulfilment details are entered.

Accounting system: Accounting then invoices the customer and collects payment. The invoices and payments may be recorded in an operational system that is different from the order entry system.

 

Question 3 - Differentiate between database management systems (DBMS) and data mining.

Ans: DBMS is a system for housing and managing a set of digital databases. On the other hand data mining is a technique or concept in computing, which deals with the fact to extract useful and previously unknown information raw data. Most of the times, these raw data are kept in very large databases.  So the data miners use existing DBMS functionalities for handling, directing and even pre-process the raw data before and during the Data Mining process. Yet, a DBMS alone cannot be used to analyse data. But a few DBMS data now have inbuilt tools or capabilities that can analyse data.There are following difference:

DBMS DMDefinition Database management

systemData mining

Data Dynamic(day to day transaction/operational data)

Static(Historical data)

Data Atomicity Data is stored at microscopic level

Data is aggregated or summarized and stored at the higher level

Normalization Normalization Databases to facilitate insertion deletion and updating

De-normalized Databases to facilitate queries and analysis

History Old data is purged or archived

Historical data stored to enable trend analysis and future predictions

Queries Simple queries and updates queries use small amounts of data(One record or few records)

Complex queries quires use large amounts of dataExample:Total annual sales for

Example:Update account balance enrol for a course

north regionTotal monthly sales for north region

Updates Updates are frequent Updates are infrequentResponse time Fast response time is

importantData must be up-to-date, consistent at all times

Transaction are slowQueries consume a lot of bandwidth

Joins in queries Joins are more and complex as tables are normalized.

Joins are few and simple as tables are de-normalized.

Data models Complex data models, many tables

Simple data models, fewer tables

Focus DBMS focus on performance

DM focus on flexibility and broader scope

Question 4 - What is Neural Network? Explain in detail.

Ans: An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.

  Use of neural networks:

Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.

Other advantages include:

Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.

Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time.

Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.

Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.

Question 5 - What is partition algorithm? Explain with the help of suitable example.

Ans: These are algorithms for partitions on the set {0, 1, . . . , n − 1}. We represent partitions abstractly as forests, i.e., a collection of trees, one tree for each block of the partition. We only need the parent information about the tree so we represent the partition as a vector V with V[i] the parent of I unless i has no parent (and so is a root), in which case V[i] is negative the size of the block with i. In this scheme the least partition would be rep- resented by the vector ⟨−1, −1, . . . , −1⟩ and the greatest partition could be represented in many ways including the vector ⟨−n, 0, . . . , 0⟩. [2] contains an elementary discussion of this type of representation of partitions. We say that a vector representing a partition is in normal form if the root of each block is the least element of that block and the parent of each nonroot is its root. This form is unique, i.e., two vectors represent the same partition if and only if they have the same normal form. The examples above are in normal form. Algorithm 1 gives a simple recursive procedure for finding the root of any element i. Note that i and j are in the same block if and only if root (i) = root (j).

procedure root (i, V)j ← V[i]if j < 0 then return(i)else return(root (j)) endifend procedure

 Algorithm 1: Finding the root

 The running time for root is proportional to the depth of i in its tree, so we would like to keep the depth of the forest small. Algorithm 2 finds the root and at the same time modifies V so that the parent of i is its root without increasing the order of magnitude of the running time. In many applications you want to build up a partition by starting with the least partition and repeatedly join blocks together. Algorithm 3 does this. Note that Algorithm 3 always joins the smaller block onto the larger block. This assures us that the resulting partition will have depth at most log2 n as the next theorem shows.

procedure root (i, V)

j ← V[i]if j < 0 then return(i)else V[i] ← root (j); return(V[i]) endifend procedure Algorithm 2: Finding the root and compressing V procedure join-blocks(i, j, V)ri ← root (i, V); rj ← root (j, V);if ri ≠ rj thensi ← −V[ri ]; sj ← −V[rj ]

if si < sj thenV[i] ← rj ; V[j] ← −(si + sj )elseV[j] ← ri ; V[i] ← −(si + sj )endifendifreturn(V)end procedure

 Algorithm 3: Join two blocks together

Theorem 1 If Algorithm 3 is applied any number of times starting with the least partition, the depth of the resulting partition will never exceed log2 n.

 Proof: Let i be a fixed node. Note an application of join-blocks increases the depth of i by at most 1 and, if this occurs, the size of the block with i is at least doubled. Thus the depth of i can be increased (by 1) from its original value of 0 at most log2 n times. This result shows that the time required to run the join-blocks–procedure m times is O(m log2 n). In [1] Tarjan has shown that, if we use the root operation given in Algorithm 2, the time required is O(mα(m)), where α is the pseudo-inverse of the Ackermann function. The Ackermann function is extremely fast growing and so α grows very slowly; in fact, α(m) ≤ 4 unless m is at least

 2 to the power 2 to the power 2 upto i times (where i is 1,2,3,4,5.....) with 65536 2’s.

By Theorem 1 we may assume that all our (representations of) partitions have depth at most log2 n. The rank of a partition (in the partition lattice Πn of an n element set) is n−k, where k is the number of blocks. The join of two partitions U and V can be found by executing join-blocks(i, U[i], V) for each i which is not a root of U. This can be done in time O(rank(U) log2 n) and so in time O(n log2 n). (Actually, such an algorithm should make a copy of V so the original V is not modified.) It is relatively easy to write O(n log2 n) time procedures for putting V into normal form and for testing if V ≤ U in Πn . Finding an O(n log 2 n) time algorithm for the meet of two partitions is a little more difficult. Algorithm 4 does this. In this algorithm, HT is a hash table. (In place of a hash table, one could use a balanced tree or some other data structure described in texts on algorithms and data structures.)

procedure meet(V1 , V2 )n ← size(V1 )for i element of Z with 0 ≤ i < n dor1 ← root (i, V1 ); r2 ← root (i, V2 )if HT[r1 , r2 ] is defined thenr ← HT[r1 , r2 ]V[r ] ← V[r ] − 1V[i] ← relseHT[r1 , r2 ] ← iV[i] ← −1endifendfor

return(V)end procedure

 Algorithm 4: Meet of two partitions

Question 6 - Describe the following with respect to Web Mining:

a. Categories of Web Mining

b. Applications of Web Mining

Ans: a. Categories of Web Mining

Web mining can be broadly defined as the discovery and analysis of useful information from the World Wide Web.

Web mining can be broadly divided into three categories:

 Web Content Mining

Web content mining targets the knowledge discovery, in which the main objects are the traditional collections of multimedia documents such as images, videos, and audio, which are embedded in or linked to the web pages.

It is also quite different from Data mining because Web data are mainly semi-structured and/or unstructured, while Data mining deals primarily with structured data. Web content mining is also different from Text mining because of the semi-structure nature of the Web, while Text mining focuses on unstructured texts. Web content mining thus requires creative applications of Data mining and / or Text mining techniques and also its own unique approaches. In the past few years, there was a rapid expansion of activities in the Web content mining area. This is not surprising because of the phenomenal growth of the Web contents and significant economic benefit of such mining. However, due to the heterogeneity and the lack of structure of Web data, automated discovery of targeted or unexpected knowledge information still present many challenging research problems.

Web Structure Mining

Web Structure Mining focuses on analysis of the link structure of the web and one of its purposes is to identify more preferable documents. The different objects are linked in some way. The intuition is that a hyperlink from document A to document B implies that the author of document. A thinks document B contains worthwhile information. Web structure mining helps in discovering similarities between web sites or discovering important sites for a particular topic or discipline or in discovering web communities.

Simply applying the traditional processes and assuming that the events are independent can lead to wrong conclusions. However, the appropriate handling

of the links could lead to potential correlations, and then improve the predictive accuracy of the learned models.

Web Usage Mining

Web Usage Mining focuses on techniques that could predict the behaviour of users while they are interacting with the WWW. Web usage mining, discover user navigation patterns from web data, tries to discovery the useful information from the secondary data derived from the interactions of the users while surfing on the Web. Web usage mining collects the data from Web log records to discover user access patterns of web pages. There are several available research projects and commercial tools that analyse those patterns for different purposes. The insight knowledge could be utilized in personalization, system improvement, site modification, business intelligence and usage characterization.

b. Applications of Web Mining

With the rapid growth of World Wide Web, Web mining becomes a very hot and popular topic in Web research. E-commerce and E-services are claimed to be the killer applications for Web mining, and Web mining now also plays an important role for E-commerce website and E-services to understand how their websites and services are used and to provide better services for their customers and users.

A few applications are:

E-commerce Customer Behaviour Analysis: Increase retention rates, customer lifetime value, and purchase frequency using customer analysis.

E-commerce Transaction Analysis: provides business transaction intelligence to the IT group that help them optimizing customer-facing services like ATMs, PoS, kiosks and e-commerce applications.

E-commerce Website Design: Create an ecommerce site design as unique as your business. Whether your site needs a makeover or a quick touch up, our professional designers will work closely with you to turn your vision into reality.

E-banking: Online banking (or Internet banking or E-banking) allows customers of a financial institution to conduct financial transactions on a secure website operated by the institution, which can be a retail or virtual bank, credit union or building society.

M-commerce: The phrase mobile commerce was originally coined in 1997 to mean "the delivery of electronic commerce capabilities directly into the consumer’s hand, anywhere, via wireless technology."[1] Many choose to think of Mobile Commerce as meaning "a retail outlet in your customer’s pocket."

Web Advertisement: Online advertising, also known as online advertisement, internet marketing, online marketing or e-marketing, is the marketing and promotion of products or services over the Internet.

Search Engine: A web search engine is a software system that is designed to search for information on the World Wide Web. The search results are generally presented in a line of results often referred to as search engine results pages.

Online Auction: An online auction is an auction which is held over the internet. Online auctions come in many different formats, but most popularly they are ascending English auctions, descending Dutch auctions, first-price sealed-bid, Vickrey auctions, or sometimes even a combination of multiple auctions, taking elements of one and forging them with another. The scope and reach of these auctions have been propelled by the Internet to a level beyond what the initial purveyors had anticipated.