Collaboration Data Retrieval Using Multi-Dimensional Cube
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Solution Overview
Problem
Individuals face difficulties in recalling and locating prior instances of email correspondences and instant messaging conversations relevant to current discussion topics, as they are often buried within large volumes of data and require significant time to search through.
Innovation Solution
A method that correlates keywords and participants from instant messaging conversations with email correspondences and prior instant messaging conversations over time, using a multi-dimensional cube to organize historical collaboration data and provide relevant prior instances, dynamically adjusting to changes in keywords and discussion topics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually search through large volumes of email and messaging data to locate prior instances, then they can find relevant information, but the time and effort required increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically collecting, storing, and indexing collaboration data from multiple sources (emails, instant messages, documents) in advance. This pre-processing creates a structured repository that enables rapid retrieval without manual searching, directly resolving the contradiction between information completeness and search time
Solution Approach 2:
The system introduces an intermediary component (the collaboration data retrieval system with multi-dimensional cube) that acts as a mediator between users and the vast repository of collaboration data. This intermediary automatically processes queries, correlates keywords across different communication channels, and presents relevant results, eliminating the need for users to manually search through raw data
2Reliability
If users review all historical communications to ensure completeness, then they can be certain of finding relevant information, but the complexity and effort of the process increases
Solution Approach 1:
The system segments the complex task of retrieving collaboration information by organizing data into distinct categories (emails, instant messages, documents) and processing them through separate collection modules. The multi-dimensional cube further segments results by participant, time period, and topic, making the complex data manageable and reliable without requiring users to handle overall complexity
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with retrieved results (such as viewing patterns, selections, and refinements) are used to improve future retrieval accuracy. This feedback loop ensures reliability by continuously refining the system's understanding of what constitutes relevant information while simplifying the user process
3Measurement precision
If the system stores and analyzes all collaboration data from multiple sources, then retrieval accuracy improves, but the quantity of data to be processed increases
Solution Approach 1:
The system extracts only the essential and relevant features from large volumes of collaboration data, such as key participants, time periods, topics, and contextual relationships. Rather than processing and storing all raw data equally, it extracts meaningful elements and organizes them in the multi-dimensional cube structure, achieving high retrieval accuracy without being burdened by the full volume of raw data
Solution Approach 2:
The system transforms the one-dimensional search problem into a multi-dimensional solution space by organizing data along multiple dimensions (participants, time, topics, communication channels). This dimensional transformation allows the system to efficiently navigate and query large volumes of data by filtering along specific dimensions, achieving high precision without proportionally increasing processing complexity
Data Source
AI summary
A computer identifies a first participant and a one or more additional participants associated with a collaboration through an electronic communication application that includes messaging content, identifies a first set of one or more keywords within the messaging content between the first participant and the one or more additional participants, retrieves a first set of historical information based at least in part on the first set of one or more keywords, the first participant and the one or more additional participants, generates a first instance of historical results that includes a multi-dimensional cube that graphs the first set of historical information with respect to the first set of one or more keywords, the first participant and the one or more additional participants, and a time frame, and provides the first instance of historical results to the first participant.