Distributed Data Enablement Platform with Search and Behavior Bots
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Solution Overview
Problem
Current computing architectures are inadequate for processing extreme data from diverse sources in real-time, as they lack the necessary hardware and software resources to handle the volume, variety, and velocity of data, and fail to provide relevant information tailored to individual user behaviors.
Innovation Solution
A computing system with a data enablement platform that employs multiple search bots and behavior bots to process data from various sources, using distributed streaming analytics and machine learning to filter and personalize results based on user behavior, and presents them through a user interface that can receive input in various forms, including voice, gestures, and facial expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional computing systems are used to process data, then system simplicity is maintained, but the system cannot handle the volume, variety, and velocity of extreme data from multiple sources
Solution Approach 1:
The system segments data processing into multiple specialized search bots, each responsible for specific data sources or data types. This division allows the system to handle extreme data volumes by distributing processing tasks across multiple independent components rather than requiring a single monolithic system.
Solution Approach 2:
The architecture employs universal behavior bots that can process and analyze data from multiple sources using the same machine learning frameworks. These behavior bots serve multiple functions by adapting to different data types and sources, reducing the need for separate specialized systems for each data source.
2Adaptability or versatility
If data from multiple diverse sources is processed, then data variety and relevance are improved, but the complexity of integrating and processing data from these sources increases
Solution Approach 1:
The system introduces intermediary components including data normalization layers and standardized communication protocols that mediate between diverse data sources and the processing bots. These intermediaries translate and standardize data from different sources into a common format, enabling seamless integration without direct complex point-to-point connections.
Solution Approach 2:
The architecture dynamically adjusts processing parameters such as data sampling rates, filtering thresholds, and analysis depths based on the characteristics of each data source. This parameter adaptation allows the system to efficiently handle diverse data types by optimizing processing settings for each source rather than using a fixed complex integration scheme.
3Speed
If real-time processing of extreme data is implemented, then data velocity handling is improved, but the computational resources and system complexity required increase
Solution Approach 1:
The system implements periodic processing cycles where search bots periodically query data sources and behavior bots periodically analyze accumulated data. This periodic action allows real-time processing capabilities while managing computational load by processing data in controlled intervals rather than continuous high-intensity computation.
Solution Approach 2:
The architecture applies partial processing by initially filtering and preprocessing data at the source level before full analysis. Behavior bots perform selective deep analysis only on data that meets certain criteria or shows promise, rather than exhaustively processing all incoming data, thus achieving real-time responsiveness with reduced computational overhead.
Data Source
AI summary
The amount and variety of data being generated is becoming too extreme for many computing systems to process, and is even more difficult for information systems to provide relevant data to users. A distributed computing system is provided that includes server machines that form a data enablement platform. The platform includes: a plurality of data collectors that stream data over a message bus to a streaming analytics and machine learning engine; a data lake and a massive indexing repository for respectively storing and indexing data; a behavioral analytics and machine learning module; and multiple application programming interfaces (APIs) to interact with the data lake and the massive indexing repository, and to interact with multiple applications. The multiple applications are command cards, and each command card includes a directive module, a memory module, search bots, and behavior bots that operate at least within the data enablement platform.