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You searched for subject:(eDiscovery). Showing records 1 – 3 of 3 total matches.

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University of Kansas

1. Haenchen, Steven L. Advanced Text Searching of Electronic Information Related to Forensic Discovery.

Degree: MS, Electrical Engineering & Computer Science, 2009, University of Kansas

The Federal Rules of Civil Procedure regarding production of electronic evidence, together with court rulings and penalties, have highlighted the need for timely and accurate production of electronically stored responsive evidence. Key criteria to the legal requirements include costs to produce, identification of responsive information and identification of privileged information within the responsive information. Currently the primary two methods of compliance are manual review of the documents and electronic Boolean text searches. Text searching technology has been studied for over fifty years generating literally thousands of documents and books for a literature review. The focus of the literature includes accuracy of searching, optimization of searching, and completeness of searching. Some of the literature is based on a specific field of interest such as library cards or patent filings, but most is either generic or relates to either peer-to-peer searching or Internet searching. The documents related to the field of electronic evidence are very limited in number and presented no new search techniques directly. We identified and classified the search techniques from the literature study after consideration of the applicability to electronic evidence. Using electronic evidence from actual litigation cases, the techniques were implemented to identify the thoroughness of the documents identified in the population and the related costs (time) required to identify such documents. The results from the various techniques were compared along with the costs to identify the "best" text searching method. Based on the results, we recommend implementation of a combination of the techniques to allow responsiveness to different requirements based on the legal circumstances. Advisors/Committee Members: Saiedian, Hossein (advisor), Agah, Arvin (cmtemember), Ercal-Ozkaya, Gunes (cmtemember).

Subjects/Keywords: Computer science; Ediscovery; Electronic information; Forensics; Text searching

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APA (6th Edition):

Haenchen, S. L. (2009). Advanced Text Searching of Electronic Information Related to Forensic Discovery. (Masters Thesis). University of Kansas. Retrieved from http://hdl.handle.net/1808/5975

Chicago Manual of Style (16th Edition):

Haenchen, Steven L. “Advanced Text Searching of Electronic Information Related to Forensic Discovery.” 2009. Masters Thesis, University of Kansas. Accessed October 19, 2019. http://hdl.handle.net/1808/5975.

MLA Handbook (7th Edition):

Haenchen, Steven L. “Advanced Text Searching of Electronic Information Related to Forensic Discovery.” 2009. Web. 19 Oct 2019.

Vancouver:

Haenchen SL. Advanced Text Searching of Electronic Information Related to Forensic Discovery. [Internet] [Masters thesis]. University of Kansas; 2009. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/1808/5975.

Council of Science Editors:

Haenchen SL. Advanced Text Searching of Electronic Information Related to Forensic Discovery. [Masters Thesis]. University of Kansas; 2009. Available from: http://hdl.handle.net/1808/5975


Université du Luxembourg

2. Ayetiran, Eniafe Festus. A Combined Unsupervised Technique for Automatic Classification in Electronic Discovery.

Degree: 2017, Université du Luxembourg

Electronic data discovery (EDD), e-discovery or eDiscovery is any process by which electronically stored information (ESI) is sought, identified, collected, preserved, secured, processed, searched for the ones relevant to civil and/or criminal litigations or regulatory matters with the intention of using them as evidence. Searching electronic document collections for relevant documents is part of eDiscovery which poses serious problems for lawyers and their clients alike. Getting efficient and effective techniques for search in eDiscovery is an interesting and still an open problem in the field of legal information systems. Researchers are shifting away from traditional keyword search to more intelligent approaches such as machine learning (ML) techniques. State-of-the-art algorithms for search in eDiscovery focus mainly on supervised approaches, mainly; supervised learning and interactive approaches. The former uses labelled examples for training systems while the latter uses human assistance in the search process to assist in retrieving relevant documents. Techniques in the latter approach include interactive query expansion among others. Both approaches are supervised form of technology assisted review (TAR). Technology assisted review is the use of technology to assist or completely automate the process of searching and retrieval of relevant documents from electronically stored information (ESI). In text retrieval/classification, supervised systems are known for their superior performance over unsupervised systems. However, two serious issues limit their application in the electronic discovery search and information retrieval (IR) in general. First, they have associated high cost in terms of finance and human effort. This is particularly responsible for the huge amount of money expended on eDiscovery on annual basis. Secondly, their case/project-specific nature does not allow for resuse, thereby contributing more to organizations' expenses when they have two or more cases involving eDiscovery. Unsupervised systems on the other hand, is cost-effective in terms of finance and human effort. A major challenge in unsupervised ad hoc information retrieval is that of vocabulary problem which causes terms mismatch in queries and documents. While topic modelling techniques try to tackle this from the thematic point of view in the sense that both queries and documents are likely to match if they discuss about the same topic, natural language processing (NLP) approaches view it from the semantic perspective. Scalable topic modelling algorithms, just like the traditional bag of words technique, suffer from polysemy and synonymy problems. Natural language processing techniques on the other hand, while being able to considerably resolve the polysemy and synonymy problems are computationally expensive and not suitable for large collections as is the case in eDiscovery. In this thesis, we exploit the peculiarity of eDiscovery collections being composed mainly of e-mail communications and their attachments, mining topics of… Advisors/Committee Members: Boella, Guido [superviser], Torre, Leon van der [superviser].

Subjects/Keywords: eDiscovery; unsupervised; classification; Engineering, computing & technology :: Computer science [C05]; Ingénierie, informatique & technologie :: Sciences informatiques [C05]

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ayetiran, E. F. (2017). A Combined Unsupervised Technique for Automatic Classification in Electronic Discovery. (Doctoral Dissertation). Université du Luxembourg. Retrieved from http://orbilu.uni.lu/handle/10993/31289

Chicago Manual of Style (16th Edition):

Ayetiran, Eniafe Festus. “A Combined Unsupervised Technique for Automatic Classification in Electronic Discovery.” 2017. Doctoral Dissertation, Université du Luxembourg. Accessed October 19, 2019. http://orbilu.uni.lu/handle/10993/31289.

MLA Handbook (7th Edition):

Ayetiran, Eniafe Festus. “A Combined Unsupervised Technique for Automatic Classification in Electronic Discovery.” 2017. Web. 19 Oct 2019.

Vancouver:

Ayetiran EF. A Combined Unsupervised Technique for Automatic Classification in Electronic Discovery. [Internet] [Doctoral dissertation]. Université du Luxembourg; 2017. [cited 2019 Oct 19]. Available from: http://orbilu.uni.lu/handle/10993/31289.

Council of Science Editors:

Ayetiran EF. A Combined Unsupervised Technique for Automatic Classification in Electronic Discovery. [Doctoral Dissertation]. Université du Luxembourg; 2017. Available from: http://orbilu.uni.lu/handle/10993/31289


Mid Sweden University

3. Carlsson, Anna. eDiscovery-samverkan för digitalt bevarande.

Degree: Information Systems and Technology, 2019, Mid Sweden University

För att lösa problemen med långsiktigt digitalt bevarande måste det finnas en samverkan mellan människan, processen och tekniken. Databearbetning är ett viktigt steg att säkra den digitala långtidslagringen av dokument, och att använda samarbetsytor med gemensamma plattformar, för utarbetning mellan arkivinstitutioner. I ett datornätverk som automatiskt ska kunna hitta och kommunicera med annan utrustning i samma nätverk så använder vi dagligen olika system för att öppna våra filer, som i det ursprungliga filformatet har ett "inbyggt" format, som på ett bestämt sätt ordnar data som ska läsas och bearbetas av ett datorprogram. Här krävs att insamlingen av materialet görs korrekt. Behovet av kontaktytor för samarbete är stor och en aktivitet efter lösningar som möjliggör bland annat att uppfylla lagstiftning om bevarande av information pågår. Myndigheter och många företag som ännu inte uppmärksammat problematiken har behovet av lösningar.

To solve the problems of long-term digital preservation, there must be an interaction between man, the process and the technology. Data processing is an important step to secure the digital long-term storage of documents, and to use collaborative surfaces with common platforms, for preparation between archive institutions. In a computer network that should automatically be able to find and communicate with other equipment in the same network, we use different systems daily to open our files, which in the original file format has a "built-in" format, which in a certain way organizes data to be read and processed by a computer program. Here, the collection of the material is required to be done correctly. The need for contact areas for collaboration is great and an activity for solutions that enable, among other things, to comply with legislation on information retention is ongoing. Authorities and many companies that have not yet noticed the problem have the need for solutions.

Subjects/Keywords: eDiscovery; Collection; Preservation; Artificial Intelligence; Modeling; Framework; Dimensions Profile; Modeling Concepts; Standards.; eDiscovery; Insamling; Bevarandesystem; Artificiell intelligens. Modelleringsmetoder; Plattform; Begreppsmodell; Dimensions Profile; Standarder.; Information Systems, Social aspects; Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Carlsson, A. (2019). eDiscovery-samverkan för digitalt bevarande. (Thesis). Mid Sweden University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-37259

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Carlsson, Anna. “eDiscovery-samverkan för digitalt bevarande.” 2019. Thesis, Mid Sweden University. Accessed October 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-37259.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Carlsson, Anna. “eDiscovery-samverkan för digitalt bevarande.” 2019. Web. 19 Oct 2019.

Vancouver:

Carlsson A. eDiscovery-samverkan för digitalt bevarande. [Internet] [Thesis]. Mid Sweden University; 2019. [cited 2019 Oct 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-37259.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Carlsson A. eDiscovery-samverkan för digitalt bevarande. [Thesis]. Mid Sweden University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-37259

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.