Yearly Archives: 2011
As 2011 draws to a close, it seems the economic crisis is set to continue, with the unreliability of the euro only exacerbating an already difficult financial landscape. This economic uncertainty means that the future is increasingly difficult to predict, …Read more
Many industries use different analytics solutions, but as the ongoing flow of relevant data is becoming more frequent we see that pure analytics does not really address the need to continuously process this data. Streaming analytics expands system’s processing capabilities …Read more
The current complex global financial markets include risks that are distributed across many entities. Credit risk in no longer limited to two parties, a bank and borrower, it now includes a variety of financial products and instruments, which makes counterparty …Read more
Nearly every move in the current business world involves some type of risk. These risks can stem from uncertainty in financial markets, project failures, legal liabilities, credit risk, or any uncertain or unpredictable cause or event. This is why organizational …Read more
When discussing PnL VaR, we refer to the implication profit and loss calculations of an enterprise, mostly financial institutions, on its value at risk measures. This data is used to provide an estimate of the amount of economic capital the …Read more
One of the major challenges in the management and measurement of risk that many financial institutions face is finding a coherent approach to value at risk aggregation. Some of the drivers behind this challenge are developments in regulatory standards and …Read more
Value at Risk (VaR) is a general tool for assessing market risk; it measures the worst expected loss over a given horizon under normal market conditions at a given level of confidence. CVaR value at risk is the most common …Read more
If you are looking into MDX, XMLA and the connection between, you arrived at the right place. We have put together this post to provide some basic information about MDX XMLA.
In this post, we’ve collected a number of typical OLAP performance issues, with tips on how to perform OLAP performance tuning.
Many businesses need the ability to aggregate and analyze complex business data in real time. This isn’t possible with traditional business intelligence solutions, which perform resource-intensive analysis and usually run in overnight batches. To overcome this limitation, several vendors have …Read more
In classic OLAP solutions, which usually store data on a hard disk, a key bottleneck is data access speed. Storing data in memory speeds up data access dramatically is a first step to performing real time OLAP analysis. In in …Read more
If you’re looking into OLAP technology, you’ve probably come across a large number of acronyms describing different flavors or types of OLAP. We’ve put together this post to help you make sense of the mess with brief definitions of each …Read more
As traditional OLAP systems are usually designed to analyze large databases of historic data, they lack the ability to process the most current events as they are introduced into the system. Streaming OLAP provides the ability to continuously monitor new …Read more
Real time OLAP, or RTOLAP, is the capability to quickly retrieve, aggregate, analyze and present multi-dimensional data for cubes whenever there are changes to the data in the relational data source, without having to run heavy batch processing on the …Read more