A computer-implementable method for cognitive information processing comprising: receiving data from a plurality of data sources, at least one of the plurality of data sources comprising a blockchain data source having associated blockchain data; processing the data from the plurality of data sources to provide cognitively processed data; performing a learning operation to improve the cognitively processed data over time, the learning operation being based at least in part on the blockchain data from the blockchain data source; and, providing the cognitively processed data to a destination.

Method for Cognitive Information Processing Using a Cognitive Blockchain Architecture

Embodiments of the disclosure describe methods and systems for improved forecasting of IT workloads and related metrics. Embodiments may include, for example, normalizing, warehousing and mining of data sets of many types collected from many different customers, applications and execution environments, and of metrics internal and external to such customers. Prediction or forecasting algorithms may be developed for predicting future IT workload demand based on a discovered relationship between certain factors and historical information. The prediction algorithms may be applied as appropriate to each customer's workloads, applications, and execution environments.

Forecasting information technology workload demand

Approaches are provided for answering an inquiry of a cognitive distributed network. An approach includes receiving the inquiry at the cognitive distributed network. The approach further includes determining a classification for the inquiry based on natural language of the inquiry. The approach further includes classifying the inquiry as a single question class. The approach further includes determining, by at least one computing device, a type of introspection to be used by the cognitive distributed network on the inquiry. The approach further includes generating an answer to the inquiry based on the determined type of introspection.

Cognitive distributed network

Mechanisms are provided for processing a knowledge canvassing request. The mechanisms receive a request specifying an entity of interest from an originator of the request and analyze the request to extract a feature of the request. The mechanisms determine whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on the extracted feature. In response to determining that the request is a knowledge canvassing request, the mechanisms process the request by identifying entities represented in a knowledge graph data structure as being related to the entity of interest. The mechanisms output results of the processing of the request to the originator of the request.

Knowledge Canvassing Using a Knowledge Graph and a Question and Answer System

Configurations of computing resources established on a web-based computing service can be monitored, managed and controlled. According to an embodiment, a configuration compliance service is implemented. Policy rules governing attributes of configuration states for computing resources in a subscriber's account are defined. The computing resources in the account are monitored for changes to configuration states. In response to detection of a change to a configuration state that violates a rule, a predefined action is taken. The predefined action can include sending a notification to the subscriber's account, reconfiguring the configuration state of the computing resource, and deactivating the computing resource.

Resource configuration compliance service

Social media inputs from users, and specific to a content of a broadcast program, are analyzed to determine a sentiment value expressed relative to a portion of the content by a user. A group is formed of those users whose sentiment values expressed relative to the portion within a defined period during the broadcast of the program are within a specified tolerance value of sentiment value expressed by the user. A time zone of a location extracted from a social media input of the user is assigned to the group. A fabricated event that evokes a predicted sentiment is inserted in the broadcast program. A confidence value for the time zone is computed by comparing a sentiment value of a response by the group to the fabricated event with the predicted sentiment value. The group's placement in the time zone is confirmed when the confidence value exceeds a threshold.

Location-based event affinity detangling for rolling broadcasts

An approach is provided for allowing a network computing (e.g., cloud computing) infrastructure to modify its resource allocation plan (e.g., an instance count) by using a Kth derivative vector plot, which may be generated using historical logs. Among other things, this approach enables an infrastructure to project an allocation forecast for a specified duration and adapt to changes in network traffic.

Resource provisioning using predictive modeling in a networked computing environment

Embodiments of the present invention provide an approach for allocating computing resources based on social networking/media trends in a networked computing environment (e.g., a cloud computing environment). In a typical embodiment, a baseline computing resource allocation will be determined for the networked computing environment based upon historical computing resource data (e.g., stored in at least one computer storage device). Social networking trend data corresponding to usage of a set of social networking websites may be received and analyzed to determine a forecasted computing resource allocation (e.g., based on social networking trends). The baseline computing resource allocation may be compared to the forecasted computing resource allocation to identify any difference therebetween. A computing resource allocation protocol/plan may then be determined based on the comparison (e.g., to address the difference).

Resource allocation based on social networking trends in a networked computing environment

For distributed processing using forecasted location-based IoT device clusters, at a central IoT device, a data source that is to be used and a duration for processing a workload is determined. A set of IoT devices operating within a threshold distance from the data source at a first time is selected. A first subset of the IoT devices is selected to form a cluster of IoT devices where each IoT device satisfies a clustering condition. A second subset of the first subset is selected to form a sub-cluster of IoT devices where a forecasted travel path of a member IoT device in the second subset keeps the member within the threshold distance from the data source for the duration. A lightweight application is configured at a first IoT device in the second subset which enables the first IoT device to participate in the sub-cluster and process the workload.

Distributed load processing using forecasted location-based internet of things device clusters

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Thomas B. Harrison

Author Tools

Chat about Author