Business Download Hyperion Intelligence Explorer 8' title='Business Download Hyperion Intelligence Explorer 8' />Bulletin SB17296 Vulnerability Summary for the Week of October 16, 2017 Original release date October 23, 2017. Essbase Wikipedia. Essbase is a multidimensional database management system MDBMS that provides a multidimensional database platform upon which to build analytic applications. Essbase, whose name derives from extended spreadsheet database, began as a product of Arbor Software, which merged with Hyperion Software in 1. Business Download Hyperion Intelligence Explorer Standard' title='Business Download Hyperion Intelligence Explorer Standard' />Business Download Hyperion Intelligence Explorer Training1 Introducing Oracle Business Intelligence Enterprise Edition. This chapter describes how to get started with Oracle Business Intelligence Enterprise Edition and. InformationWeek. com News, analysis and research for business technology professionals, plus peertopeer knowledge sharing. Engage with our community. Oracle Corporation acquired Hyperion Solutions Corporation in 2. Oracle marketed Essbase as Oracle Essbase an on premises product and more recently, Essbase is offered as part of the Oracle Analytics Cloud. Until late 2. 00. IBM also marketed the product as DB2 OLAP Server. The database researcher E. F. Codd coined the term on line analytical processing OLAP in a whitepaper2 that set out twelve rules for analytic systems an allusion to his earlier famous set of twelve rules defining the relational model. SV11.1.2.3_OBIEE/images/t10101.gif' alt='Business Download Hyperion Intelligence Explorer Tutorial' title='Business Download Hyperion Intelligence Explorer Tutorial' />This whitepaper, published by Computerworld, was somewhat explicit in its reference to Essbase features, and when it was later discovered that Codd had been sponsored by Arbor Software, Computerworld withdrew the paper. In contrast to on line transaction processing OLTP, OLAP defines a database technology optimized for processing human queries rather than transactions. The results of this orientation were that multidimensional databases oriented their performance requirements around a different set of benchmarks Analytic Performance Benchmark, APB 1 than that of RDBMS Transaction Processing Performance Council TPC. Hyperion renamed many of its products in 2. Essbase an official name of Hyperion System 9 BI Analytic Services, but the new name was largely ignored by practitioners. The Essbase brand was later returned to the official product name for marketing purposes, but the server software still carried the Analytic Services title until it was incorporated into Oracles Business Intelligence Foundation Suite BIFS product. In August 2. Information Age magazine named Essbase as one of the 1. SelfStudy Courses Hectic schedule Download training courses take them from your computer, at your own pace. Startup Tools Click Here 2. Lean LaunchPad Videos Click Here 3. FoundingRunning Startup Advice Click Here 4. Market Research Click Here 5. Life Science Click. Netscape, the Black. Berry, Google, virtualization, Voice Over IP VOIP, Linux, XML, the Pentium processor, and ADSL. Editor Kenny Mac. Iver said Hyperion Essbase was the multi dimensional database technology that put online analytical processing on the business intelligence map. It has spurred the creation of scores of rival OLAP products and billions of OLAP cubes. History and motivationeditAlthough Essbase has been categorizedby whom as a general purpose multidimensional database, it was originally developed to address the scalability issues associated with spreadsheets such as Lotus 1 2 3 and Microsoft Excel. Indeed, the patent covering Essbase uses spreadsheets as a motivating example to illustrate the need for such a system. In this context, multi dimensional refers to the representation of financial data in spreadsheet format. A typical spreadsheet may display time intervals along column headings, and account names on row headings. For example Jan. Feb. Mar. Total. Quantity. Sales1. 002. 003. Expenses8. 01. 602. Profit2. 04. 06. If a user wants to break down these values by region, for example, this typically involves the duplication of this table on multiple spreadsheets North. Jan. Feb. Mar. Total. Quantity. 24. 01. Sales2. 41. 8952. Expenses2. 01. 5031. Profit43. 924. South. Jan. Feb. Mar. Total. Quantity. 76. Sales7. Expenses6. 01. 02. Profit1. 615. 87. Total Region. Jan. Feb. Mar. Total. Quantity. Sales1. 002. 003. Expenses8. 01. 602. Profit2. 04. 06. An alternative representation of this structure would require a three dimensional spreadsheet grid, giving rise to the idea that Time, Account, and Region are dimensions. As further dimensions are added to the system, it becomes very difficult to maintain spreadsheets that correctly represent the multi dimensional values. Multidimensional databases such as Essbase provide a data store for values that exist, at least conceptually, in a multi dimensional hypercube. SparsityeditAs the number and size of dimensions increases, developers of multidimensional databases increasingly face technical problems in the physical representation of data. Say the above example was extended to add a Customer and Product dimension Dimension. Number of dimension values. Accounts. 4Time. 4Region. Customer. 10,0. 00. Product. 5,0. 00. If the multidimensional database reserved storage space for every possible value, it would need to store 2,4. If the software maps each cell as a 6. GB. In practice, of course, the number of combinations of Customer and Product that contain meaningful values will be a tiny subset of the total space. This property of multi dimensional spaces is referred to as sparsity. AggregationeditOLAP systems generally provide for multiple levels of detail within each dimension by arranging the members of each dimension into one or more hierarchies. A time dimension, for example, may be represented as a hierarchy starting with Total Time, and breaking down into multiple years, then quarters, then months. An Accounts dimension may start with Profit, which breaks down into Revenue and Expenses, and so on. In the example above, if Product represents individual product SKUs, analysts may also want to report using aggregations such as Product Group, Product Family, Product Line, etc. Similarly, for Customer, natural aggregations may arrange customers according to geographic location or industry. The number of aggregate values implied by a set of input data can become surprisingly large. If the Customer and Product dimensions are each in fact six generations deep, then 3. It follows that if all these aggregate values are to be stored, the amount of space required is proportional to the product of the depth of all aggregating dimensions. For large databases, this can cause the effective storage requirements to be many hundred times the size of the data being aggregated. Block storage Essbase AnalyticseditSince version 7, Essbase has supported two storage options which take advantage of sparsity to minimize the amount of physical memory and disk space required to represent large multidimensional spaces. The Essbase patent6 describes the original method, which aimed to reduce the amount of physical memory required without increasing the time required to look up closely related values. With the introduction of alternative storage options, marketing materials called this the Block Storage Option Essbase BSO, later referred to as Essbase Analytics. Put briefly, Essbase requires the developer to tag dimensions as dense or sparse. The system then arranges data to represent the hypercube into blocks, where each block comprises a multi dimensional array made up of dense dimensions, and space is allocated for every potential cell in that block. Sparsity is exploited because the system only creates blocks when required. In the example above, say the developer has tagged Accounts and Time as dense, and Region, Customer, and Product as sparse. If there are, say, 1. Region, Customer and Product that contain data, then only 1. Accounts and Time. The number of cells stored is therefore 1. MB, plus the size of the index used to look up the appropriate blocks. Because the database hides this implementation from front end tools i. Ignore Elon Musk and Mark Zuckerbergs War Over Killer Robots, the Real Challenge Is Already Here. Tech giants Elon Musk and Mark Zuckeberg have been engaged in a very public, somewhat silly and self indulgent battle over artificial intelligence lately. Musk has warned AI powered robots could usher in some form of automated war to give humanity its richly deserved demise, while Zuckerberg responded by saying he is really optimistic it could usher a golden age of lifesaving technology. Both have traded blows, with Zuckerberg saying the doomsaying is pretty irresponsible, and Musk tweeting he thinks the Facebook head just doesnt understand the issue. The whole thing is a little eyeroll inducing given true AI remains a pipe dream for now, and both men stand to benefit greatly from machine learning trends which automate jobs and concentrate control of the emerging digital economy in fewer hands. Coursera cofounder Andrew Ng, a real AI researcher who used to be chief scientist at Chinese tech company Baidu, weighed in on the latter issue Tuesday. As an AI insider, having built and shipped a lot of AI products, I dont see a clear path for AI to surpass human level intelligence, Ng told attendees at a Harvard Business Review event, Venture. Beat reported. I think that job displacement is a huge problem, and the one that I wish we could focus on, rather than be distracted by these science fiction ish, dystopian elements. Ive been in a lot of private conversations with AI leaders, or business leaders who are working on new AI products that will wipe out tens of thousands of jobs in a single company, maybe more across multiple companies, he added. And the interesting thing is that a lot of people whose jobs are squarely in the crosshairs of the technologies, a lot of people doing the jobs that are about to go away, they dont understand AI, they dont have the training to understand AI. Filezilla 3 3 0 1 Win32 Setup New Email. And so a lot of people whose jobs are going to go away dont know that theyre in the crosshairs, Ng concluded. Some 5. 6 million US manufacturing jobs disappeared from 2. Center for Business and Economic Research at Ball State University, with approximately 8. Estimates vary, but they largely agree that a huge slice of US jobs could be lost to technologysomewhere between 3. That falls in line with Ngs warning early AI like technologies such as machine learning are putting a lot of jobs commonly assumed to be the continued domain of humans at risk. Researchers in the field have been sounding this warning for years. A 2. 01. 3 Oxford University study on computerisation identified a number of high risk professions ranging from traffic technicians and medical records technicians to loan officers, nuclear reactor operators and technical writers. Risk is just risk, and some recent research suggests automation and computerization of US jobs has slowed down lately in part because wages have remained so low workers are cheaper than fancy new toys. But on another point, Ng is right Polls show Americans tend to be optimistic that this trend wont kill their jobs, just those of other poor saps. That did not work out so well for the taxi industry. High concept futurist drama over whether or not machines will kill or save us all grab headlines. But the more immediate concern is already here Figuring out how to keep the economy working for most of us before people like Musk and Zuckberg run away with all our money.