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We encountered several locks due to this (our ops team didn't want to use real clustering, so all DB weren't updated).
#Ibm watson news explorer use case update
Third: We needed to update all the databases and keep old data status.
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Second: We needed several load balanced Solr databases.So we need to be sure to update and match the 2 databases in the same time. Then we have all data in our MySQL DB and searching data in Solr. This bulletin provides a remediation for the vulnerability, CVE-2021-44228 by upgrading Watson Explorer and thus addressing the exposure to the log4j vulnerability. First: We couldn't keep all our data in Solr. Summary Log4j is used by IBM Watson Explorer to log system events for diagnostics.We have lot of classifications and lot of data for each classification.High data volumes of data and a lot of users were the causes. I think we have had the same problems with other NoSQL databases (but perhaps not the same solution). These examples are due to the way we use Apache Solr.OSS/BSS (Operations Support/Business Support).Security Orchestration, Automation and Response (SOAR).Integration Platform as a Service (iPaaS).car paint) on production lines.Īnne Aussems is global growth initiative lead – alliances at Capgemini. Our solution substitutes human inspection of typical quality defects (i.e. Combining robotics, IoT, visual recognition and NLP technologies, we are supporting customers with robotized testing of cockpits and other control stations.ġ6. Our service desk offering includes automation of typical call intents that can be handled by a chatbot. We are augmenting the capabilities of our staffing resources to identify the best resources for a project with a tool that reads resumes, projects descriptions, and identifies the best candidates throughout the organization.ġ4. For an airline, we are piloting the use of a robot to assist travelers at airports.ġ3. For a public service, we built a service crawling inputs in social media apps to gather valuable insight on improvement areas.ġ2. Cases included: improving decision factors to perform imaging on children in ER quality insurance on compliance with cancer treatment pathways identification of cancer “companion diseases,” underestimated medication side effects and comorbidity factors. For hospitals, we conducted multiple studies to improve treatment decisions based on analysis of years’ worth of anonymized patient files. For a life sciences player, we performed market analysis, identifying competitive information, market trends, and more.ġ0. Retail automation. To address the key pain point in the shoe-buying process (finding a sales rep in the store, waiting for them to go check the inventory, etc.), we built an app recognizing shoe models, checking availability in the inventory and prompting a sales person to bring the right model to the customer. Shopping assistant. We built a smart conversational app that helps consumers with their online purchases.Ĩ. HR chatbot. We helped a customer build a chatbot answering the most typical HR questions.ħ. ~1%), measuring several compliance and quality measures, detecting fraud earlier and gathering new customer satisfaction insight.Ħ. For a telco customer, we automated audit calls, making it possible to listen to 100% of the calls (vs. The sentiment analysis capabilities of Watson were a key differentiator to accurately define whether a company was being viewed in positive terms. Startup watch. For a bank, we created a tool supporting automation of the examination of startup companies. Claims management agent assist. For insurers, we built solutions bringing insurance policy details and other enterprise documentation to the fingertips of call center agents handling claims, to help them reply to their customer’s detailed questions in real time.Ĥ. For an online banking client, we integrated their chatbot with Salesforce to enable the chatbot to retrieve customer information and address most customer requests, with the objective of the chatbot becoming an effective cross sell engineģ. For an insurer, we have built a virtual agent complementing their customer services team to answer customer questions on insurance products, coverages, policies, etc.Ģ.