Analysis system using brokers that access information sources
Inventors
Barrett, Christopher L. • Marathe, Madhav V. • Bisset, Keith R.
Assignees
Virginia Polytechnic Institute and State University
Publication Number
US-9870531-B2
Publication Date
2018-01-16
Expiration Date
2030-04-14
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Abstract
Systems, methods, and computer-readable media for generating a data set are provided. One method includes generating a data set based on input data using a plurality of brokers. The method further includes receiving a request from a user and determining whether the request can be fulfilled using data currently in the data set. When the request can be fulfilled using data currently in the data set, the data is accessed using broker(s) configured to provide access to data within the data set. When the request cannot be fulfilled using data currently in the data set, at least one new broker is spawned using existing broker(s) and additional data needed to fulfill the request is added to the data set using the new broker. The method further includes generating a response to the request using one or more of the plurality of brokers.
Core Innovation
The invention relates to systems, methods, and computer-readable media for generating a data set using a plurality of brokers. The system utilizes multiple broker software modules that access information sources, collect data, and integrate it into a data set representing populations or other complex structures. Upon receiving a request, the system determines if it can fulfill the request using available data; if not, it spawns new broker modules to retrieve additional required data. This approach provides a scalable and flexible framework for integrating heterogeneous data sources and fulfilling user or system requests dynamically.
The problem addressed by the invention is the difficulty in constructing large-scale computer models, particularly social contact networks or interaction models, due to challenges in collecting reliable data from diverse sources. Existing approaches often rely on limited data sets or indirect inference, which can be insufficient for complex, heterogeneous scenarios involving multiple data providers and sensitive or proprietary datasets.
By dynamically spawning brokers to access different information sources and by constructing a composite data set that reflects a synthetic or modeled population, the system allows detailed and customizable situation representation. Each broker manages access to specific data resources, and the broker architecture controls information partitioning, resource sharing, and workflow delegation in fulfilling data requests. This system is applicable to a wide variety of domains where integration of data from multiple sources is required for complex modeling and analysis tasks.
Claims Coverage
The independent claims of the patent define several inventive features relating to the use of broker software modules to access disparate information sources, fulfill requests, and generate result data or data sets.
Spawning brokers to retrieve information from multiple sources
The invention includes spawning at least a first and second broker software module. Each broker is configured with instructions to retrieve information from a different information source. This enables independent collection from distinct sources, both for initial data acquisition and for dynamic addition when a request requires further data.
Fulfilling requests by orchestrating broker modules and generating result data
Upon receiving a request for result data, the system determines the required data elements from different sources, directs respective broker modules to retrieve their portion of the data, and then generates the result data by combining the collected information. This orchestration can also involve determining when existing data is insufficient and spawning additional brokers as needed.
Partitioned data access and control via broker structure
The system implements data set partitioning, where different broker modules have access rights limited to different data partitions. This structure supports controlled access, security, and modularity, enabling brokers to access only their assigned data while supporting coordination through dedicated modules (e.g., coordination broker, information exchange mechanisms).
Dynamic spawning of new brokers when requests cannot be fulfilled by existing data
The architecture supports determining when a request cannot be fulfilled with currently available data. When such a determination is made, the system spawns a new broker to retrieve information from a new source to fulfill the unmet portions of the request. This dynamic expansion can also involve spawning service brokers and edge brokers in hierarchical fashion.
Non-direct communication between information sources mediated by brokers
Information sources, including computer-executable applications, do not communicate directly with each other. Instead, brokers are responsible for running these applications or accessing their data and mediating the information transfer, ensuring modularity and separation between sources.
In summary, the claims establish inventive features centered around a dynamic, modular broker-based system architecture for retrieving, integrating, partitioning, and combining information from multiple distinct sources, with flexible spawning and mediation of broker software modules to meet varying and complex data requests.
Stated Advantages
The system enables integration of data from at least two distinct information sources, improving flexibility and scalability in building complex data sets.
Requests can be fulfilled dynamically by spawning brokers as needed, allowing responsive adaptation to varying data requirements without prior knowledge of all possible resources.
Partitioning data access via brokers supports controlled and secure information flow, including cases involving proprietary or sensitive data.
The broker architecture allows for modular, reusable components that can be orchestrated for complex modeling tasks without requiring direct communication between disparate data sources.
Once data has been integrated into the synthetic data set, future similar representations can be generated using fewer computing resources, increasing efficiency.
Documented Applications
Modeling complex social contact networks and interactions among entities in large-scale populations (e.g., urban areas, infrastructures).
Simulating and analyzing traffic flow and transportation networks using demographic and activity-based data from multiple sources.
Wireless spectrum allocation simulation, such as for government agencies determining radio frequency spectrum use.
Generating synthetic populations for scenario-based experiments, such as epidemiological studies or infrastructure planning.
Analyzing and supporting context-based decision-making based on composite situation representations involving diverse data types.
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