IT ARCHIVE Executive Committee Chief Patron : Thiru. E. R. Kaarthikeyan M.A., Patron : Dr. N. Rajagopal M.Sc., M.Phil., Ph.D., PGDCA. D.A., Editor in Chief : Prof. D. Gayathri Devi M.C.A., M.Phil, Staff Advisor NAVIGATOR Mr. S. Vijayakumar M.C.A., M.Phil. Lecturer (SS), Department of M.C.A., VOL-21 Multidimensional Database 3 StafStafff Editor Mrs. V.Valli Mayil M.C.A., M.Phil., Lecturer (SG), Department of M.C.A., Linux Focus 13 Organizing Members Mr. Ram Kumar.N II-MCA. Technologies Mr. Narayan Chalise II-MCA. -Quick time X 18 Ms. Sabitha.E II-MCA. Ms. Brindha .C II-MCA. Mr. Madhan kumar.M II-MCA. Mr. Madhan.D II-MCA. Intellect Explore 22 Mr.Karthikeyan.R I-MCA. I Mr.Balukumar.P I-MCA. Mr.Raffic Raja.R I-MCA. Career Objectives 24 Ms.Soundariya.T I-MCA. Ms.Srilakshmi.T.V I-MCA. Windows 7 26 Company Profile 29 Tips and Tricks 30 Quotable Words Monthly Event Gist 32 There's many a bestseller that could have been prevented by a good teacher. Fill the unforgiving minute with sixty seconds worth of distance run. The truth is more important than the facts. 1 ACKNOWLEDGEMENT We wish to thank Thiru. E.R.Kaarthikeyan, M.A., Correspondent, Kongu Arts and Science College, Erode and our Management for the support to publish the magazine. Dr. N. Rajagopal M.Sc., M.Phil., Ph.D., PGDCA., D.A., Principal, Kongu Arts and Science College, has provided considerable support to us during this effort. We proudly thank our Chief Editor, Staff Advisor, Staff Editor, Staff members and the students of Department of M.C.A for their guidance and suggestions to complete the magazine. 2 MULTIDIMENSIONAL DATABASE Introduction The usage of multidimensional database in internet application is very effective because A multidimensional database is a of the data can be accessed quickly and form of database that is designed to make succinctly. For instance, when an end user the best use of storing and utilizing data. want to determine how many widget sales Usually structured in order to optimize are generated during the third quarter of the online analytical processing (OLAP) and past year in a given sales territory, this data data warehouse applications, the could be obtained from a Multidimensional multidimensional database can receive data database with a simple question by asking from a variety of relational databases and “How may widgets were sold during third structure the information into categories and quarter 2007 within the Southwest sections that can be accessed in a number of Territory?” the end user does not have to go different ways. Even persons who have through the steps of building a report, relatively little experience working with a specifying fields, and restricting content database often find that a multidimensional within those fields in selection criteria. The database, or MDB, requires only a short use of one simple question will accomplish time to master. While just about every the task of generating criteria. relational database is structured for keyword searches and building a query by specifying Online 0Analytical 0Processing fields and perimeters, the multidimensional (OLAP) database goes one step further. Rather then building a query, a user simply poses the Online analytical processing, or OLAP , is question in everyday verbiage. This an approach used to quickly answer multi- approach is used with several online help dimensional analytical queries. OLAP is part tools associated with software programs of the broader category of business such as word processing and spreadsheet intelligence, which also encompasses applications, as well as several of the more relational reporting and data mining. The popular search engines currently in use. typical applications of OLAP are in business 3 reporting for sales, marketing, management Date/Time label that describes more about reporting, business process management that sale. (BPM), budgeting and forecasting, financial More number of dimensions can be added to reporting and similar areas. The term OLAP the structure such as Store, Cashier, or was created as a slight modification of the Customer by adding a column to the fact traditional database term OLTP. Databases table. This allows an analyst to view the configured for OLAP use a measures along any combination of the multidimensional data model, allowing for dimensions . complex analytical and ad-hoc queries with a rapid execution time. They borrow aspects The output of an OLAP query is typically of navigational databases and hierarchical displayed in a matrix (or pivot) format. The databases that are faster than relational dimensions form the rows and columns of databases. The core of any OLAP system is the matrix are the measures form values. the concept of an OLAP cube (also called a multidimensional cube or a hypercube ). It Multidimensional databases consists of numeric facts called measures which are categorized by dimensions . The Multidimensional structure is defined as “a cube metadata is typically created from a variation of the relational model that uses star schema or snowflake schema of tables multidimensional structures to organize data in a relational database. Measures are and express the relationships between data”. derived from the records in the fact table and The structure is broken into cubes and the dimensions are derived from the dimension cubes are able to store and access data. tables. “Each cell within a multidimensional structure contains aggregated data related to Each measure can be thought of as having a elements along each of its dimensions”. set of labels , or meta-data associated with it. Even when data is manipulated it is still easy A dimension describes the labels ; it provides to access and store the compact type of information about the measure . database. The data still remains interrelated. Multidimensional structure is quite popular A simple example would be a cube that for analytical databases that use online contains a store's sales as a measure , and analytical processing (OLAP) applications. Date/Time as a dimension . Each Sale has a 4 Analytical databases use these databases • Automated computation of higher because of their ability to deliver answers level aggregates of the data. quickly to complex business queries. Data • It is very compact for low dimension can be seen from different ways, which data sets. gives a broader picture of a problem unlike • Effective data extract achieved other models. through the pre-structuring of aggregated data. Types Disadvantages of MOLAP OLAP systems have been traditionally categorized using the following taxonomy. • The processing step (data load) can be quite lengthy, especially on large Molap data volumes. This is usually remedied by doing only incremental MOLAP stands for Multidimensional processing, i.e., processing only the Online Analytical Processing. data which has changed (usually new MOLAP is the 'classic' form of OLAP and data) instead of reprocessing the is sometimes referred to as just OLAP. entire data set. MOLAP stores the data in optimized multi- • MOLAP tools traditionally have dimensional array storage, rather than in a difficulty querying models with relational database. dimensions with very high cardinality (i.e., millions of Advantages of MOLAP members). • Some MOLAP products have • Fast query performance due to difficulty in updating and querying optimized storage, multidimensional models with more than ten indexing and caching. dimensions. This limit differs • Smaller on-disk size of data depending on the complexity and compared to data stored in relational cardinality of the dimensions in database due to compression question. It also depends on the techniques. number of facts or measures stored. 5 Other MOLAP products can handle Therefore, ROLAP still involves creating an hundreds of dimensions. additional copy of the data. However, since • MOLAP approach introduces data it is a database, a variety of technologies can redundancy. be used to populate the database. Relational Advantages of ROLAP ROLAP stands for Relational Online • ROLAP is considered to be more Analytical Processing. scalable in handling large data volumes, especially models with ROLAP works directly with relational dimensions with very high databases. The base data and the dimension cardinality (i.e. millions of tables are stored as relational tables and new members). tables are created to hold the aggregated information depends on a specialized • With a variety of data loading tools schema design. available, and the ability to fine tune the ETL code to the particular data ROLAP does not require the pre- model, load times are generally computation and storage of information. much shorter than with the Instead, ROLAP tools access the data in a automated MOLAP loads. relational database and generate SQL queries to calculate information at the • The data is stored in a standard appropriate level when an end user requests relational database and can be it. With ROLAP, it is possible to create accessed by any SQL reporting tool additional database tables ( summary tables (the tool does not have to be an or aggregations ) which summarize the data OLAP tool). at any desired combination of dimensions. • ROLAP tools are better at handling While ROLAP uses a relational database non-aggregately facts (e.g. textual source, generally the database must be descriptions). MOLAP tools tend to carefully designed for ROLAP use. A suffer from slow performance when database which was designed for OLTP will querying these elements. not function well as a ROLAP database. 6 • By decoupling the data storage from Hybrid the multi-dimensional model, it is possible to successfully model data There is no clear agreement across the that would not otherwise fit into a industry as to what constitutes "Hybrid strict dimensional model. OLAP", except that a database will divide data between relational and specialized Disadvantages of ROLAP storage. For example, for some vendors, a HOLAP database will use relational tables • There is a consensus in the industry to hold the larger quantities of detailed data, that ROLAP tools have slower and use specialized storage for at least some performance than MOLAP tools.