Cost and efficiency remain core areas of focus when approaching Electronically Stored Information (ESI) from a document management perspective.

During the process various questions arise such as:

  • How to optimize the accuracy of the data set eligible for review?
  • How to filter out the majority of irrelevant material?

Data managers can rely on search utilities to build a refined data set. To determine what search operation yields the least amount of documents without over-exclusion, a data manager can benefit from taking full advantage of search function "best practices".

ESI databases place words and characters in an index for optimal search performance. The ability to retrieve a desired term can depend on:

  • Similar content in the data set
  • Possible derivative forms of the term
  • Opportunities for the term to have alternative meanings

These considerations can result in the development of a “search plan” that considers the literal term usage within the document text.  However, other questions to consider include how the search engine interprets the:

  • Terms in the search structure
  • Related terms in the source data
  • Resulting index of the retrieval universe

A maximized search structure will consider both the source data and how the host system handles the search process.

To evaluate the effectiveness of search criteria, an initial assessment of the tokenization requirements will quickly identify areas for further consideration.

A “tokenized” character will read as a space during the indexing or search process, even though it will display as a text character.

Conversely, a “non-tokenized” symbol will read as presented in text.  

Any tokenized characters identified in the initial assessment warrant evaluation regarding best syntax to refine the search results.

The most logical question following this topic is why one would want operations treating symbols differently?  

One example is the following scenario:

Suppose a case has a subpoena that states the exact words, e-mail addresses, and characters that should be produced. 

In this instance, using a tokenized operator one would generate overly inclusive results. When a data manager has an idea of what to look for within a data set, but has questions regarding how a word should appear, a tokenized operator will provide variable components to determine what should and should not remain in the data set.

 

Table 1 - How Tokenized characters affect Indexing and Searching

 

Capital Novus provides powerful modules to take advantage of tokenization in ESI data management with eZSuite’s eZReview and eZVUE. These modules use various operators to customize searching on data sets and within metadata fields to provide flexibility to leverage tokenization for refined searching. Indexing routines match fields with tokenized schemes for best results based on the type of information expected for the associated field.

 

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Terms and Conditions

Terms and conditions for training courses

I will abide to follow following terms and conditions once registering a course with Institute of Forensic Science, Gujarat Forensic Sciences University in association with Capital Novus,

1. Face-to-face courses normally run with a suitable minimum of participants. Should a course be cancelled because of shortage of participants, you will be offered a place on an alternative course or a full refund of fees paid.
2. Confirm that you are willing to participate fully in the course.
3. Agree to pay the relevant fees.
4. Agree not to distribute the work or material or lecture notes or any other related to this course (online or hard copy) without permission in written from Institute of Forensic Science, Gujarat Forensic Sciences University.
5. Agree to take full responsibility for your actions and opinions.
6. Confirm that you have, or are willing to secure access to, relevant materials where the course necessitates this.
7. All courses must be completed within stipulated time decided by the Institute of Forensic Science, Gujarat Forensic Sciences University and if fail to complete within stipulated time or fail to attend regular theory and / or practical session , admission will be cancelled and to continue, fees need to be paid.
8. Refunds will not be payable after a course has been started.
9. While registering the course, candidate must provide an accurate and complete postal address, contact telephone number and e-mail address. We may refuse or cancel your enrolment if you do not supply these. Your details will not be used for any purpose other than the efficient and effective running of Real Group Ltd.
10. If circumstances arise that are beyond our control, it may be necessary from time to time to change/cancel course dates, content, venues and prices from those published. Whilst we will make every effort to transfer your booking to the next available course at your preferred venue, it should be noted that we will not be held liable for any costs/losses incurred as a result of any such changes. If we are no longer able to provide your course, we will ask you to return any course materials to us (at our expense) in the condition as originally delivered to you and refund to you any fees paid to date when we receive the materials as required.
11. The majority of correspondence with and from Real Group Ltd is conducted electronically using e-mail and web-based protocols. Your details will be added automatically to our database. Your details will not be passed to any third party without your permission, unless requested by law or a similar authority.

Institute of Forensic Science, Gujarat Forensic Sciences University in association with Capital Novus reserves the rights to change or amendment in the terms and conditions from 01 to 11 time to time and I agree to abide same