Volume (Scalability)

Scalability is a key consideration for enterprises wishing to protect themselves from information risks, as the performance of many technologies degrades significantly as the system size increases. Key challenges of scalability include large and globally dispersed numbers of users and volumes of information, the need for real-time results around the clock with minimal downtime and the rapid and unpredictable growth in content.

How Do Manual Legacy Techniques Approach the Problem of Scalability?

In order to hide scalability limitations, some legacy technologies query only a fraction of an index and prematurely end the searching process as soon as a set number of results is retrieved. Similarly, some technologies only search through a select number of words in the document, with the assumption that the key content is captured in the first few pages. In the consumer search space both of these are generally accepted techniques for generating fast query response times. However, in the enterprise, searching only a fraction of the index severely compromises the integrity of the search and exposes organizations to risk.

How Does Autonomy ZANTAZ Resolve the Challenge of Scalability?

Autonomy ZANTAZ provides predictable scaling methods on a huge scale and does not use any tricks to give the illusion of scalability. Autonomy ZANTAZ searches the entire index and the full content of every document. In addition to linear scalability, IDOL's modular architecture has many benefits including: easy addition of new modules, minimal disruption during maintenance or disturbance, ability to use modules in redundant configurations and ability to distribute modules across machines and platforms locally and globally. IDOL can handle massive amounts of data on commodity dual-CPU servers. For instance, only a few hundred entry-level enterprise machines are required to support ChoicePoint's 10-billion record footprint. By comparison, a competitor uses 150,000 machines to handle the same amount of data. This enhanced scalability results in hardware cost-savings as well as the ability to address larger volumes of content.

Though IDOL scales extremely well on commodity servers, its flexible architecture can take full advantage of massive parallelism, SMP processing capabilities, 64-bit environments (such as Intel Itanium 64-bit architecture), software platforms (such as Solaris 10, Linux 64, Win64, etc), distributed server farms and all common forms of external disk arrays (i.e. NAS, SAN etc) to further improve performance.

Autonomy powers some of the largest systems in the world:

• • ChoicePoint uses Autonomy to index 10 billion documents

  • Bloomberg uses Autonomy across all 120 million, multilingual news articles including 300 million emails and 1.26 million trading alerts per day

  • Over 120,000 users within GlaxoSmithKline use Autonomy to access 200 terabytes of data in multiple formats

  • BBC Online uses Autonomy to archive millions of documents in over 33 languages and is used by 20 million people per day

  • America Online provides millions of users with real-time access to news articles, video and audio content, entertainment pages, shopping tips and classifieds

  • The US Department of Defense uses IDOL to manage the knowledge base accessed by over five million employees

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