Brainspace: Quick Reference

Transcription

1 Brainspace is a dynamic and flexible data analysis tool. The purpose of this document is to provide a quick reference guide to navigation, use, and workflow within Brainspace. This guide is divided into three sections: (1) a workflow overview for using Brainspace, which presents a good starting point when diving into a dataset for the first time, (2) a reference guide to the available Brainspace features, and (3) case study for how the tool has been used in an investigation/eca context. Additional detailed guides and advanced user trainings are available upon request. Workflow Overview Step 1: Document Collections and Tagging. Create collections in order to tag and collect documents for specific purposes. Common examples include: Not relevant, Hot, specific issues, etc. If opted to include a Relativity database alongside a Brainspace database, users can sync these collections tagging to Relativity ICA or Review databases once documents have been added to collections, so that quick searching in Relativity can also be performed on these tags and for ease of tracking during review. Step 2: Data Visualization and Objective Culling. Upon logging into Brainspace, users will be able to see a visual representation of the data. Users can hone in on a specific time frame, and remove duplicate documents not caught by standard MD5 deduplication. Step 3: Keyword and Concept Searching. Once users narrow the data by size and create tags, users can now search the documents using either keyword or concept based searching. Concept search can include a single word, or a conceptually relevant sentence/paragraph up to 40,000 characters. Keyword search is done through Lucene syntax. Step 4: Cluster Wheel. The cluster wheel is used to see the different concepts and key terms that were found in the data population being explored. This will allow the user to look at the most common words found in the documents and it will allow the ability to dive into specific areas of communication efficiently. Step 5: Communication Mapping and Analysis. The communication analysis tool will then allow users to see the various people involved in the communications being explored. Brainspace Features Collections Collections provide a shareable way to group and track documents of relevance or interest within a data set. Collections can be setup at any time and users can create new collections and add documents to existing collections as they cull and analyze the data set. These collections are found on the collections tab and can also be synced to tags within a Relativity ICA (culling) or Review workspace, if opted to include on the matter.

2 Brainspace Dashboard Overview The case Dashboard serves as a home page for the matter s data. It provides a high level overview of the data and it is the starting point for analysis. It is also where navigation begins within Brainspace. Quick Filtering Brainspace provides the ability to quickly filter from the Dashboard by selecting document categories from the Data Overview, a date range from the Timeline or through selection of a term, address, domain or other metadata category from the Top occurrence selectors.

3 Searching Brainspace provides functionality to perform concept, keyword, and conditional searching from the Search tab. Concept Search Brainspace s Transparent Semantic Search converts free text into a list of related terms and phrases, which are presented to users for review and weighting purposes post search. This is an intuitive way to enter text (word, sentence, or phrase) to be converted into concept tokens and is available in the search interface. JAVA JAVA JUNIT JAVA OR JUNIT QUERY STRING +JAVA +JUNIT JAVA AND JUNIT TITLE:ANT TITLE:EXTREME SUBJECT:SPORTS (AGILE OR EXTREME) AND JAVA TITLE: JUNIT IN ACTION TITLE: JUNIT ACTION ~5 JAVA* JA?A Advanced Search Advanced search in Brainspace allows users to utilize search for specific addresses, domains, keywords and metadata field queries. If users are looking for a specific address or domain, this is the quickest way to perform searching on the same. Utilizing the Advanced Query Builder within the advanced searching will allow for the use of AND, OR, and NOT connectors to search across multiple metadata fields. The Keyword search feature Lucene search syntax. Keywords are automatically searched with stemming turned on. The following search functions will be available when utilizing keyword search: RETURNS DOCUMENTS THAT: CONTAIN THE TERM JAVA IN THE DEFAULT FIELD CONTAIN THE TERM JAVA OR JUNIT OR BOTH IN THE DEFAULT FIELD (THE DEFAULT OPERATOR CAN BE CHANGED TO AND) CONTAIN BOTH JAVA AND JUNIT IN THE DEFAULT FIELD CONTAIN THE TERM ANT IN THE TITLE FIELD CONTAIN EXTREME IN THE TITLE AND NOT SPORTS IN SUBJECT BOOLEAN EXPRESSION MATCHES EITHER AGILE OR EXTREME AND JAVA PHRASE MATCHES IN TITLE PROXIMITY MATCHES (JUNIT WITHIN 5 WORDS OF ACTION) IN TITLE WILDCARD MATCHES JAVA~ FUZZY MATCHES Tip: For searching for addresses, it is best practice to search for the ID and the domain using a within 2 proximity search. For example: If users are looking for john@yahoo.com, the search should be john yahoo ~2.

4 Saved Searches Upon culling and filtering the data set and returning desired results, creating a saved search can provide a future reference. These will appear under the Saved Searches drop down. Note: All saved searches are private and not shareable to others. Cluster Wheel The Cluster Wheel groups documents together based on concept words/phrases listed in bold black text within the Concept Cluster. This feature allows the user to visualize the data, and navigate documents that have similar content. If searches have been run, the user can see where these hits reside within the documents clusters by the red indicators on the Cluster Wheel. Tip: The Brainspace cluster, by default, shows the cluster of the entire data universe. If there s a want to create a cluster based on a subset of the total data universe, a focus set can be created. Focus Sets Focus Sets can be used when there is a specific sub set of documents within the data set that analysis should be limited within. This could be a simple date range limitation, or an in depth culled data set determined relevant which requires further analysis.

5 Communication Analysis The Communication Analysis is a feature that visually graphs people and domains and their communications between one another. Users can quickly switch between people and domain analysis, see the direction of communication and a summary of what s been discussed via Top Terms, and manage the number of recipients. Tip: Limiting the number of recipients to an helps avoid any s being sent to a large distribution of people, which tend to be less relevant in most cases. Advanced Brainspace Features: Predictive Coding & Relativity Sync Brainspace provides additional features by syncing Brainspace with Relativity. These features include: Predictive coding and Continuous Active Learning (CAL/TAR 2.0) Sync Brainspace collection with Relativity tags. This allows for: o Using Brainspace to minimize and identify a document population for review in Relativity. o Using Brainspace to analyze documents tagged in Relativity for quality assurance (e.g. Responsive, Non Responsive, Hot Documents, Privilege, etc.). threading and near duplicate analysis. If predictive coding, or more information about the Relativity Sync features is desired, Discovia can provide a demo or additional documentation upon request. Training Videos Brainspace training videos can also be found on the Discovia website at the following link: category/brainspace/

6 Case Study The Situation The United States might be investigating how government funded projects were awarded to various companies. Our client, who was awarded with one of these contracts, wanted to quickly access and review communications surrounding the bidding process in order to gauge any potential misconduct. On Feb. 2nd, our client sent Discovia 15 GB of data for a key custodian to investigate. The Result: Our client was able to find all key documents within 2 days and save $9, in review. By Feb. 3rd, the data was ready in Brainspace for our client to review. By Feb. 4th, a single reviewer was able to gather all key communications and complete his review. This same review would have taken 7 days when using more traditional tools. 2 Days to complete in Brainspace 7 Days using traditional ediscovery tools and workflows The Process Data Visualization and Objective Culling: Upon logging into Brainspace, our client was able to see a visual representation of the data. He was then able to hone in on a specific time frame, remove duplicate documents not caught by standard MD5 deduplication, and further reduce the data through advance filters. Client immediately reduced the total volume by 75% (removing 30,969 documents). Communication Mapping and Analysis: Our client was then able to use the communication analysis tool to place various people that were involved in the bidding process and place them into specific groups. This allowed our clients to significantly refine the dataset further by focusing on communication between these groups. This step reduced the total volume by another 22% (removing another 9,085 documents). Cluster Wheel and Concept Searching: The cluster wheel then allowed our client to visually see the different concepts and key terms that were found in the most relevant 3% of communications he wanted to analyze. The tool allowed him to look at documents at a high level, use the wheel to dive deeper into specific areas, and use concept search of key phrases and sentences to find hot documents as well as validate his results.