Oracle metadataFrom Wikipedia, the free encyclopediaJump to: navigation, search

The introduction to this article provides insufficient context for those unfamiliar with the subject. Please help improve the article with a good introductory style. (October 2009)

Oracle metadata is information contained within the Oracle Database about the objects contained within. You can use this information to find all tables accessible by a user, get a list of stored procedures, and get information about many other types of objects in an Oracle database.

The ORACLE application server and Oracle relational database keep metadata in two areas: data dictionary tables (accessed via built-in views) and a metadata registry. The total number of these views depends on the Oracle version, but is in a 1000 range.

The few main built-in views accessing Oracle RDBMS data dictionary tables are:

ALL_TABLES - list of all tables in the current database that are accessible to the current user

ALL_TAB_COLUMNS - list of all columns in the database that are accessible to the current user

ALL_ARGUMENTS - lists the arguments of functions and procedures that are accessible to the current user

ALL_ERRORS - lists descriptions of errors on all stored objects (views, procedures, functions, packages, and package bodies) that are accessible to the current user

ALL_OBJECT_SIZE - included for backward compatibility with Oracle version 5 ALL_PROCEDURES - (from Oracle 9 onwards) lists all functions and procedures (along

with associated properties) that are accessible to the current user ALL_SOURCE - describes the text (i.e. PL/SQL) source of the stored objects accessible

to the current user

In addition there are equivalent views prefixed "USER_" which show only the objects owned by the current user (i.e. a more restricted view of metadata) and prefixed "DBA_" which show all objects in the database (i.e. an unrestricted global view of metadata for the database instance). Naturally the access to "DBA_" metadata views requires specific privileges.

Contents

[hide]

1 Example 1: finding tables 2 Example 2: finding columns 3 Example 3: counting rows of columns

4 Use of underscore in table and column names 5 Oracle Metadata Registry 6 See also 7 External links

[edit] Example 1: finding tables

Find all Tables that have PATTERN in the table name

SELECT TABLE_NAME FROM ALL_TABLES WHERE TABLE_NAME LIKE '%PATTERN%' ORDER BY TABLE_NAME;

[edit] Example 2: finding columns

Find all tables that have at least one column that matches a specific PATTERN in the column name

SELECT TABLE_NAME, COLUMN_NAME FROM ALL_TAB_COLUMNS WHERE COLUMN_NAME LIKE '%PATTERN%';

[edit] Example 3: counting rows of columns

Estimate a total number of rows in all tables containing a column name that matches PATTERN (this is SQL*Plus specific script)

COLUMN DUMMY NOPRINT COMPUTE SUM OF NUM_ROWS ON DUMMY BREAK ON DUMMY SELECT NULL DUMMY, T.TABLE_NAME, C.COLUMN_NAME, T.NUM_ROWS FROM ALL_TABLES T, ALL_TAB_COLUMNS C WHERE

T.TABLE_NAME = C.TABLE_NAME AND C.COLUMN_NAME LIKE '%PATTERN%' ORDER BY T.TABLE_NAME;

Note that NUM_ROWS records the number of rows which were in a table when (and if) it was last analyzed. This will most likely deviate from the actual number of rows currently in the table.

[edit] Use of underscore in table and column names

The underscore is a special SQL pattern match to a single character and should be escaped if you are in fact looking for an underscore character in the LIKE clause of a query.

Just add the following after a LIKE statement:

ESCAPE '_'

And then each literal underscore should be a double underscore: __

Example

LIKE '%__G' ESCAPE '_'

[edit] Oracle Metadata Registry

The Oracle product Oracle Enterprise Metadata Manager (EMM) is an ISO/IEC 11179 compatible metadata registry. It stores administered metadata in a consistent format that can be used for metadata publishing. As of January 2006, EMM is available only through Oracle consulting services.

MetadataFrom Wikipedia, the free encyclopedia

Jump to: navigation, search

For the page on metadata in Wikipedia, see Wikipedia:Metadata.

This article needs attention from an expert on the subject. See the talk page for details. WikiProject Libraries may be able to help recruit an expert. (March 2010)

This article needs attention from an expert on the subject. See the talk page for details. WikiProject Computing or the Computing Portal may be able to help recruit an expert. (February 2009)

Metadata is loosely defined as data about data. Metadata is a concept that applies mainly to electronically archived or presented data and is used to describe the a) definition, b) structure and c) administration of data files with all contents in context to ease the use of the captured and archived data for further use. For example, a web page may include metadata specifying what language it's written in, what tools were used to create it, where to go for more on the subject and so on.

Contents

[hide]

1 Metadata definition o 1.1 Libraries o 1.2 Photographs o 1.3 Video o 1.4 Web pages o 1.5 Creation of metadata

2 Metadata structures o 2.1 Metadata syntax o 2.2 Metadata types o 2.3 Hierarchical, linear and planar schemata o 2.4 Metadata hypermapping o 2.5 Granularity

3 Metadata standards 4 Metadata usage

o 4.1 Statistics and census services

o 4.2 Library and information science o 4.3 Metadata and the law

4.3.1 United States o 4.4 Metadata in healthcare o 4.5 Metadata and data warehousing

5 Metadata on the Internet o 5.1 Geospatial metadata o 5.2 Cloud applications

6 Metadata administration and management o 6.1 Metadata storage o 6.2 Database management

7 See also 8 References 9 External links

[edit] Metadata definition

Metadata is defined as data providing information about one or more other pieces of data, such as:

means of creation of the data purpose of the data, time and date of creation, creator or author of data, placement on a network (electronic form) where the data was created, what standards used etc.

For example: A digital image may include metadata that describes how large the picture is, the color depth, the image resolution, when the image was created, and other data. A text document's metadata may contain information about how long the document is, who the author is, when the document was written, and a short summary of the document.

Metadata is data. As such, metadata can be stored and managed in a database, often called a registry or repository. However, it is impossible to identify metadata just by looking at it. We don't know when data is metadata or just data.[1]

[edit] Libraries

Metadata has been used in various forms as a means of cataloging archived information. The Dewey Decimal System employed by libraries for the indexing of books is an early example of metadata usage. This system used small 3x5 inch cards to display a book's title, author, subject matter, and a brief plot synopsis along with an abbreviated alpha-numeric identification system which indicated the physical location of the book within the library's shelves. Such data helps classify, aggregate, identify, and locate a particular book. Another form of older metadata

collection is the use by US Census Bureau of what is known as the “Long Form." The Long Form asks questions that are used to create demographic data to create patterns and to find patterns of distribution. [2] The term was coined in 1968 by Philip Bagley, one of the pioneers of computerized document retrieval.[3][4] Since then the fields of information management, information science, information technology, librarianship and GIS have widely adopted the term. In these fields the word metadata is defined as “data about data”.[5] While this is the generally accepted definition, various disciplines have adopted their own more specific explanation and uses of the term.

For the purposes of this article, an "object" refers to any of the following:

a physical item such as a book, CD, DVD, map, chair, table, flower pot, etc an electronic file such as a digital image, digital photo, document, program file, database table

etc

[edit] Photographs

Metadata may be written into a digital photo file that will identify who owns it, copyright & contact information, what camera created the file, along with exposure information and descriptive information such as keywords about the photo, making the file searchable on the computer and/or the Internet. Some metadata is written by the camera and some is input by the photographer and/or software after downloading to a computer.

Photographic Metadata Standards are governed by organizations that develop the following standards. They include, but are not limited to:

IPTC Information Interchange Model IIM (International Press Telecommunications Council), IPTC Core Schema for XMP, XMP - Extensible Metadata Platform (an Adobe standard) Exif - Exchangeable image file format, Maintained by CIPA (Camera & Imaging Products

Association) and published by JEITA (Japan Electronics and Information Technology Industries Association)

Dublin Core (Dublin Core Metadata Initiative -DCMI) PLUS (Picture Licensing Universal System)

[edit] Video

Metadata is particularly useful in video, where information about its contents (such as transcripts of conversations and text descriptions of its scenes) are not directly understandable by a computer, but where efficient search is desirable.

[edit] Web pages

Web pages often include metadata in the form of meta tags. Description and keywords meta tags are commonly used to describe the Web page's content. Most search engines use this data when adding pages to their search index.

[edit] Creation of metadata

Metadata can be created either by automated information processing or by manual work. Elementary metadata captured by computers can include information about when a file was created, who created it, when it was last updated, file size and file extension.

[edit] Metadata structures

Metadata is typically structured according to a standardised concept using a well defined metadata scheme, including: metadata standards and metadata models. Tools such as controlled vocabularies, taxonomies, thesauri, data dictionaries and metadata registries can be used to apply further standardisation to the metadata.

[edit] Metadata syntax

Metadata syntax refers to the rules created to structure the fields or elements of metadata.[6] A single metadata scheme may be expressed in a number of different markup or programming languages, each of which requires a different syntax. For example, Dublin Core may be expressed in plain text, HTML, XML and RDF.[7]

[edit] Metadata types

The metadata application is manifold covering a large variety of fields of application there are nothing but specialised and well accepted models to specify types of metadata. Bretheron & Singley (1994) distinguish between two distinct classes: structural/control metadata and guide metadata.[8] Structural metadata is used to describe the structure of computer systems such as tables, columns and indexes. Guide metadata is used to help humans find specific items and is usually expressed as a set of keywords in a natural language. According to Ralph Kimball metadata can be divided into 2 similar categories - Technical metadata and Business metadata. Technical metadata correspond to internal metadata, business metadata to external metadata. Kimball adds a third category named Process metadata. On the other hand, NISO distinguishes between three types of metadata: descriptive, structural and administrative. [5] Descriptive metadata is the information used to search and locate an object such as title, author, subjects, keywords, publisher; structural metadata gives a description of how the components of the object are organised; and administrative metadata refers to the technical information including file type. Two sub-types of administrative metadata are rights management metadata and preservation metadata.

[edit] Hierarchical, linear and planar schemata

Metadata schemas can be hierarchical in nature where relationships exist between metadata elements and elements are nested so that parent-child relationships exist between the elements. An example of a hierarchical metadata schema is the IEEE LOM schema where metadata elements may belong to a parent metadata element. Metadata schemas can also be one dimensional, or linear, where each element is completely discrete from other elements and classified according to one dimension only. An example of a linear metadata schema is Dublin

Core schema which is one dimensional. Metadata schemas are often two dimensional, or planar, where each element is completely discrete from other elements but classified according to two orthogonal dimensions.[9]

[edit] Metadata hypermapping

In all cases where the metadata schemata exceed the planar depiction, some type of hypermapping is required to enable display and view of metadata according to chosen aspect and to serve special views. Hypermapping frequently applies to layering of geographical and geological information overlays.[10]

[edit] Granularity

Granularity is a term that applies to data as well as to metadata. The degree to which metadata is structured is referred to as its granularity. Metadata with a high granularity allows for deeper structured information and enables greater levels of technical manipulation however, a lower level of granularity means that metadata can be created for considerably lower costs but will not provide as detailed information. The major impact of granularity is not only on creation and capture, but moreover on maintenance. As soon as the metadata structures get outdated, the access to the referred data will get outdated. Hence granularity shall take into account the effort to create as well as the effort to maintain.

[edit] Metadata standards

International standards apply to metadata. Much work is being accomplished in the national and international standards communities, especially ANSI (American National Standards Institute) and ISO (International Organization for Standardization) to reach consensus on standardizing metadata and registries.

The core standard is ISO/IEC 11179-1:2004 [11] and subsequent standards (see ISO/IEC_11179). All yet published registrations according to this standard cover just the definition of metadata and do not serve the structuring of metadata storage or retrieval neither any administrative standardisation.

[edit] Metadata usage

[edit] Statistics and census services

Standardisation work has had a large impact on efforts to build metadata systems in the statistical community. Several metadata standards are described, and their importance to statistical agencies is discussed. Applications of the standards at the Census Bureau, Environmental Protection Agency, Bureau of Labor Statistics, Statistics Canada, and many others are described. Emphasis is on the impact a metadata registry can have in a statistical agency.

[edit] Library and information science

Libraries employ metadata in library catalogues, most commonly as part of an Integrated Library Management System. Metadata is obtained by cataloguing resources such as books, periodicals, DVDs, web pages or digital images. This data is stored in the integrated library management system, ILMS, using the MARC metadata standard. The purpose is to direct patrons to the physical or electronic location of items or areas they seek as well as to provide a description of the item/s in question.

More recent and specialised instances of library metadata include the establishment of digital libraries including e-print repositories and digital image libraries. While often based on library principles the focus on non-librarian use, espcially in providing metadata means they do not follow traditional or common cataloguing approaches. Given the custom nature of included materials metadata fields are often specially created e.g. taxonomic classification fields, location fields, keywords or copyright statement. Standard file information such as filesize and format are usually automatically included.

Standardisation for library operation has been a key topic in international standardisation (ISO) for decades. Standards for metadata in digital libraries include Dublin Core, METS, MODS, DDI, ISO standard Digital Object Identifier (DOI), ISO standard Uniform Resource Name (URN), PREMIS schema, and OAI-PMH. Leading libraries in the world give hints on their metadata standards strategies.[12][13]

[edit] Metadata and the law

[edit] United States

Problems involving metadata in litigation in the United States are becoming widespread.[when?] Courts have looked at various questions involving metadata, including the discoverability of metadata by parties. Although the Federal Rules of Civil Procedure have only specified rules about electronic documents, subsequent case law has elaborated on the requirement of parties to reveal metadata.[14] In October 2009, the Arizona Supreme Court has ruled that metadata records are public record.[15]

Document Metadata has proven particularly important in legal environments in which litigation has requested metadata, which can include sensitive information detrimental to a party in court.

Using metadata removal tools to "clean" documents can mitigate the risks of unwittingly sending sensitive data. This process partially (see Data remanence) protects law firms from potentially damaging leaking of sensitive data through Electronic Discovery.

[edit] Metadata in healthcare

Australian researches in medicine started a lot of metadata definition for applications in health care. That approach offers the first recognised attempt to adhere to international standards in medical sciences instead of defining a proprietary standard under the WHO umbrella first.

The medical community yet did not approve the need to follow metadata standards despite respective research.[16]

[edit] Metadata and data warehousing

Data warehouse (DW) is a repository of an organization's electronically stored data. Data warehouses are designed to manage and store the data whereas the Business Intelligence (BI) focuses on the usage of data to facilitate reporting and analysis.[17]

The purpose of a data warehouse is to house standardized, structured, consistent, integrated, correct, cleansed and timely data, extracted from various operational systems in an organization. The extracted data is integrated in the data warehouse environment in order to provide an enterprise wide perspective, one version of the truth. Data is structured in a way to specifically address the reporting and analytic requirements.

An essential component of a data warehouse/business intelligence system is the metadata and tools to manage and retrieve metadata. Ralph Kimball [18] describes metadata as the DNA of the data warehouse as metadata defines the elements of the data warehouse and how they work together.

[edit] Metadata on the Internet

The HTML format used to define web pages allows for the inclusion of a variety of types of metadata, from basic descriptive text, dates and keywords to further advanced metadata schemes such as the Dublin Core, e-GMS, and AGLS[19] standards. Pages can also be geotagged with coordinates. Metadata may be included in the page's header or in a separate file. Microformats allow metadata to be added to on-page data in a way that users don't see, but computers can readily access.

Interestingly, many search engines are cautious about using metadata in their ranking algorithms due to exploitation of metadata and the practice of search engine optimization, SEO, to improve rankings. See Meta element article for further discussion.

[edit] Geospatial metadata

Metadata that describe geographic objects (such as datasets, maps, features, or simply documents with a geospatial component) have a history dating back to at least 1994 (refer MIT Library page on FGDC Metadata). This class of metadata is described more fully on the Geospatial metadata page.

[edit] Cloud applications

With the availability of Cloud applications, which include those to add metadata to content, metadata is increasingly available over the Internet.

[edit] Metadata administration and management

[edit] Metadata storage

This section does not cite any references or sources.Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (June 2010)

Metadata can be stored either internally, in the same file as the data, or externally, in a separate file. Metadata that is embedded with content is called embedded metadata. A data repository typically stores the metadata detached from the data. Both ways have advantages and disadvantages:

Internal storage allows transferring metadata together with the data it describes; thus, metadata is always at hand and can be manipulated easily. This method creates high redundancy and does not allow holding metadata together.

External storage allows bundling metadata, for example in a database, for more efficient searching. There is no redundancy and metadata can be transferred simultaneously when using streaming. However, as most formats use URIs for that purpose, the method of how the metadata is linked to its data should be treated with care. What if a resource does not have a URI (resources on a local hard disk or web pages that are created on-the-fly using a content management system)? What if metadata can only be evaluated if there is a connection to the Web, especially when using RDF? How to realize that a resource is replaced by another with the same name but different content?

Moreover, there is the question of data format: storing metadata in a human-readable format such as XML can be useful because users can understand and edit it without specialized tools. On the other hand, these formats are not optimized for storage capacity; it may be useful to store metadata in a binary, non-human-readable format instead to speed up transfer and save memory.

[edit] Database management

Each relational database system has its own mechanisms for storing metadata. Examples of relational-database metadata include:

Tables of all tables in a database, their names, sizes and number of rows in each table. Tables of columns in each database, what tables they are used in, and the type of data stored in

each column.

In database terminology, this set of metadata is referred to as the catalog. The SQL standard specifies a uniform means to access the catalog, called the INFORMATION_SCHEMA, but not all databases implement it, even if they implement other aspects of the SQL standard. For an example of database-specific metadata access methods, see Oracle metadata. Programmatic access to metadata is possible using APIs such as JDBC, or SchemaCrawler.[20]

Short URL: http://con.st/10005101 

branded

These Are The Top 10 Brands In The Whole Wide World

By Chris Morran on April 28, 2010 3:28 PM 0 views

For five years, the people at BrandZ (you know they're in branding because of the "Z") have been evaluating customer opinion and awareness of various global brands, and then putting a dollar value on that evaluation for their annual Top 100 Most Valuable Global Brands report. Here are the top 10 brands on the BrandZ list.

1. Google"Google is now the number 1 brand by far with a value of $114.3 billion, on the strength of a 32 percent compounded annual growth rate since 2006, when Google ranked number 7, with a brand value of $37.4 billion."

2. IBM "The IBM brand experienced 24 percent compounded annual growth rate during the past five years and now, with a value of $86.3 billion, ranks second in the BrandZ Top 100, having advanced from position number 8."

3. Apple "Apple, now ranked number 3, with a brand value of $83.2 billion, moved up 26 places [over a five-year period] from number 29."

4. Microsoft Microsoft remained unchanged from 2008 with a value of $76.4 billion.

5. Coca Cola With a value of $67.98 billion, the Coca Cola brand remains the top beverage brand by a long shot. The closest on the list is Budweiser at #38. Pepsi ranked #58 on the list.

6. McDonald's The Golden Arches slipped just 1% from last year's value, but it was still the only fast food restaurant in the top 50. Subway, the only other fast food brand on the list was ranked #64.

7. Marlboro At a value of $57 billion, the Marlboro brand actually increased in value by 15% over the previous year.

8. China Mobile The world's largest mobile network is also the highest-rated non-U.S. brand on the list.

9. General Electric GE's brand value slipped 25% from the previous year, but it was still enough to keep the brand in the top 10.

10. Vodafone The UK-based mobile carrier is the highest-ranking European brand on the top 100.

Other brands of note: *Walmart barely missed out on the top 10, with a #13 ranking. *BMW, at #25, is the highest-ranking automobile manufacturer, edging out Toyota -- the world's largest car maker -- which ranked 26th.*The highest-ranking U.S.-based bank on the list is Wells Fargo at #30, with Bank of America coming in at #37 on the list.*While Comcast may be America's largest cable company -- and the Worst Company In America for 2010 -- it is nowhere on the top 100 list. Perhaps the Xfinity rebranding is needed after all?

My Experience:- Shruthi Rajan,BIMMby Prof Col A Balasubramanian on Sunday, August 29, 2010 at 12:59pm

XYZ Technologies(Name changed) campus recruitment was held on 25th January 2010. The job profile and remuneration package had attracted a large number of students from all the four colleges of SBS and on the D day almost 250 students were attending the process.

 

<span> </span>XYZ Technologies selection and recruitment team comprised of 10 members. They were aiming to assess candidates for both their marketing and BFSI division. The PPT began with a general description of the company, job profile and remuneration (kept subject to an upward revision for candidates with work experience). They further explained the selection process in detail and added the proviso that they were looking for candidates with a consistent 65% academic record. This condition was perhaps the first point of elimination. They also specified that since the recruitment drive had begun from our campus so there won’t be any constraints on the number of candidates they would hire provided they were the correct fit for the job.

 

<span> </span>The selection process comprised of only two rounds - GD and PI. For the GD round they bi-furcated the lot of candidates depending upon their specialization i.e. Marketing and Finance. Then each group was sub-divided into groups comprising 10 members each. I was in the second GD group. The GD was conducted and evaluated by four panelists. Since we were specifically organized into specialization specific group, we were expecting topics which would focus on Finance.

 

<span> </span>After we were summoned in for our GD, we were asked to introduce ourselves in the sequence of our seating. Since I was the first in the row so I had an abrupt beginning. I spoke about my educational background, graduation and finally post graduation. I went on to speak about my parents, sibling (brief), place of origin and hobbies. I must have hardly spoken for a minute or so. One of the panelists was a light hearted gentleman who took interest in our responses and was curious about our hobbies, family backgrounds etc and asked questions whenever something caught his attention. Rest of the three seemed interested and attentive but dint ask questions at this point.

 

<span> </span>After this they gave us the GD topic. Our topic was - ‘how does currency exchange rates affect our economy’. They gave us 2 minutes to focus our thoughts and pen down

the bullet points. Since it was a topic of my interest, I could gather my understanding of the issue in less than a minute and was ready with the correct points to begin the GD but unfortunately I missed the opportunity and somebody else stole the thunder. She spoke about how the exchange rate mechanics work and how a rise and fall in Rs against dollar affects exporters and importers. Another candidate spoke about the recent purchase of gold by India from IMF and how that would change the Rs/$ equilibrium. Meanwhile I was trying my level best to put forth my view on the topic but it so happened that by the time some candidate was finishing, someone else was already speaking. Finally I spoke. I must have been the fifth or the sixth person to speak, that too with the aid of a generous girl who let me speak. That was my chance and I had good three points to speak on. I started with the Balance of Trade situation in our country. I said that we were facing a BOT deficit as our receipts (exports) were much less than our payments (imports). In such a mismatched situation a fall in the rupee would cause a marginal improvement in our BOT through exchange earnings but a rise in the rupee would have a larger impact on BOT in terms of outward payments for imports. Further India had huge foreign obligations with regard to the loans taken from IMF and World Bank for infrastructure development and sectoral growth oriented activities. These loans ranged into billions of dollars and thus an adverse movement of currency could inflate our payables to an extent that it could crash our economy. Lastly, our foreign exchange reserve is dominated by foreign currency specifically US dollars thus a relative unfavorable movement of Rs with dollar or any other prime currency could lead to detrimental revaluation of our Forex reserves.

With this I finished and felt delighted because the panelists seemed to have listened to my points, but then they interrupted the next speaker and remarked that all speakers were addressing the panel when instead they should have been discussing the issue amongst themselves. We kept that in mind and the GD continued. After the first few minutes the GD had relaxed to an agreeable discussion where no one had to raise a voice to be heard and everybody had ample amount of time to put forth their views without someone interrupting their string of thought. I must have spoken twice after my first chance only to contribute some crucial points here and there. The topic was such that there was little or no disagreements on the viewpoints. Most of the candidates put forth intelligent and correct facts to which we agreed and added. Thus we could easily build up a consensual conclusion. Keeping aside the beginning of the discussion I never felt the need of aggressiveness in the GD. After almost half an hour one of the panel members asked a candidate (who had contributed less to the GD) to summarize incorporating all our ideas. After he finished, customarily one of us gave a unanimous conclusion. The panel members expressed their surprise at our lack of disagreement, wished us luck and let us out. When the results came three including me, from my GD group, were selected.

 

<span> </span>The next group to go in got ‘Stock exchanges as a mirror of economy’ as their GD topic. It was again a finance focused topic. So by and large they seemed to assessing our knowledge and awareness rather than our ability to lead or dominate. The third group created a chaos and none got selected for the PI round.

 

<span> </span><span> </span>Out of the total strength of 60 Finance students who sat in the interview, 20 were selected for the next round of personal interview. We were all asked to gather in a room and strict instructions were given against any of us moving out before we were called for the interview, which were held in the adjoining room. After a long, weary and tormenting wait I was called in.

 

<span> </span>This time the panel comprised of a domain specialist and an HR representative of XYZ Technologies. They both looked solemn and detached from any form of pleasantry. I wished them good evening (it was 7pm by then) and asked permission to sit down. Without loosing much time the interviewer started scanning through my CV. He saw that I had a science background, so started with teaser questions like what is the speed of an electron, how long would it take for a bulb in USA to turn on if the switch was in India and if there is a train 150 meters long then how long would it take for the last bogie to pass a specific point if the train started moving. These were sheer logic based questions and he was not expecting a certain standard answer from me. Though I got baffled in the beginning but soon enough I could retrieve my senses and put forth a reasonable answer.

 

<span> </span><span> </span>Since I had done graduation in Law, the next few questions were related to company law. He asked me about the concept of ‘Lifting of the corporate veil’ and ‘Insider trading’. He stumbled upon a certain section in Company Law Act, which I could barely recollect if not explain. I expressed my regret for being unable to remember the correct content of the section. Thereafter he moved on to asking me about the subjects I had studied in the previous semesters during the course. I could give him a few and as expected he began with a string of financial ratios, asking me in depth - the interpretation, relevance and ideal value for each ratio. I explained as many as I could remember but got stuck on one. He then asked me a lot of questions regarding my CPS project. I had submitted a report on functional aspects of Letter of Credit transactions. He asked me about the International regulatory authority for LCs and some relevant regulations regarding the same. Then he asked me to explain all the types of LCs, which I could answer satisfactorily. Further he related LC with Bank Guarantee and raised several issues in the usage of both the instruments. I could answer all these successfully.

 

<span> </span>The next batch of questions concentrated on my knowledge about the financial services like factoring, forfeiting, lease (types of lease), And Hire purchase etc. Surprisingly not a single question on capital markets, US meltdown, current affairs or banking was asked. The interview focused largely on my academic knowledge and presence of mind.

 

<span> </span>After this he gave me a situational analysis where I was supposed to be a representative of the company (manufacturing fire extinguishers) meeting an overseas client to

understand her expectations from the product i.e. fire extinguisher for her spinning mill. I was supposed to ask her relevant questions and pen down all the specifications necessary for processing her order. I did a fairly good job. After I finished the interviewer picked holes in my questions, pointed out my mistakes and suggested the best alternatives to the client’s responses. At this point he was in way making efforts to acquaint me with client transactions which was critical to a business analyst job.

Next few questions were posed by the HR representative of the company. She inquired about my family background, parents profession and why after law I chose MBA. One of the interests written in my CV was reading novels and watching movies. She asked me if I had watched any recent movie. I told her about the Hindi movie ‘3 idiots’. Upon which she reflected upon the student – management relationship and about the Chetan Bhagat controversy clouding the movie. At this point we were having a rather relaxed talk, where I expressed my views freely. She then asked me if I had read any of his novels and about his first novel i.e. ‘Five point someone’. I had read all of Chetan Bhagat’s novels, so I spoke with a lot of ease and confidence.

 

<span> </span>After finishing with the formal interview they asked me if I had any problem about relocating to any part of India or abroad. To which I said ‘No’. They asked if I had any queries about the company or the job profile. The interviewer asked me if I was aware of all the business divisions, verticals, products and services of the company. He asked me about the specific products and services offered by XYZ Technologies. My previous reading of the company profile helped greatly at this stage.

 

<span> </span>Once I answered all questions, the interviewer bided me luck and concluded the interview. As I began to leave, the interviewer expressed his disapproval about having a formal uniform in an MBA college reflecting that it inhibited free expression, to which I had nothing to say.

With this I finished my interview and came out relieved but apprehensive about the results. Finally it was past 11.30pm when the results were announced and 22 lucky candidates were given an offer to join on board XYZ Technologies.

 

Shruthi Rajan

BIMM 08045

Finance (2008-10 Batch)