Dublin, Ireland
Dublin, Ireland

Accenture plc is a multinational management consulting, technology services, and outsourcing company. Its incorporated headquarters are in Dublin, Ireland since September 1, 2009. It is the world's largest consulting firm as measured by revenues and is a Fortune Global 500 company. As of 2014, the company reported net revenues of $30.0 billion with approximately 319,000 employees, serving clients in more than 200 cities in 56 countries. Accenture has more employees in India than in any other country; in the US, it has about 40,000 employees and 35,000 located in the Philippines. Accenture's current clients include 89 of the Fortune Global 100 and more than three-quarters of the Fortune Global 500.Accenture common equity is listed on the New York Stock Exchange, under the symbol ACN, and was added to the S&P 500 index on July 5, 2011. Wikipedia.

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A signal analysis system matches a mobile device cellular identifier to a mobile device network identifier. The system obtains multiple cellular positioning records (CPRs) and multiple network positioning records (NPRs). These records include different types of information because they are generated by different types of networks. The system performs spatial matching and temporal matching of the CPRs and the NPRs to identify a specific mobile device cellular identifier among the CPRs that belongs to a mobile device identified by a specific mobile device network interface identifier among the NPRs.

Multi-data analysis based proactive defect detection and resolution may include analyzing operational data for an application to determine whether a functionality related to the application is below a predetermined threshold associated with the functionality related to the application, and based on the analysis, generating an indication to perform defect analysis related to the functionality related to the application. A sentiment analysis may be performed on consumer data related to the application to determine a sentiment of the consumer data related to the application, and a natural language processing (NLP) analysis may be performed on the consumer data related to the application to determine a function associated with a negative sentiment. Application code and process data related to the application may be analyzed to determine a defect associated with the application. Further, a code of the application may be modified to correct the defect associated with the application.

A device may receive, from a source device, first information relating to programs provided by a media content service provider. The programs may include media content. The device may generate a program schedule based on the first information. The program schedule may identify a program. The device may publish the program schedule for enrichment using second information that is different from the first information, and may receive the second information based on publishing the program schedule. The second information may include information aggregated from external source devices that are different from the source device. The device may generate an enriched program schedule using the program schedule and the second information.

Accenture | Date: 2017-02-22

A device may obtain first information related to network devices of a network. The device may obtain second information related to the network devices and/or to one or more historic network service incidents. The one or more historic network service incidents may be related to network services provided in association with the network devices. The one or more historic network service incidents may include outages and/or degradations of one or more network services. The device may perform an analysis of the first information and the second information. The device may train a predictive model based on the analysis of the first information and the second information. The predictive model may predict a probability of a future network service incident based on the first information and/or the second information. The device may cause third information, related to the network devices, to be monitored based on the predictive model.

A method for decommissioning an application operating on a computer system or a computer system includes receiving, at a decommissioning system, information that specifies a server to analyze. The decommissioning system determines one or more applications operating on the server, one or more instruction code libraries that are being utilized by the one or more applications, and hardware dependencies of the one or more instruction code libraries. Based on the determined hardware dependencies, the decommissioning system determines whether the application is suitable for migration to a cloud computing system. The decommissioning system generates a report indicating a suitability for migration of the application to the cloud computer system.

According to examples, inventory, growth, and risk prediction using image processing may include receiving a plurality of images captured by a vehicle during movement of the vehicle along a vehicle path. The images may include a plurality of objects. The images may be pre-processed for feature extraction. A plurality of features of the objects may be extracted from the pre-processed images by using a combination of computer vision techniques. A parameter related to the objects may be determined from the extracted features. A spatial density model may be generated, based on the determined parameter and the extracted features, to provide a visual indication of density of distribution of the objects related to a portion of the images, and/or to provide an alert corresponding to the objects related to the portion of the images.

Accenture | Date: 2017-03-01

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining threat data contextualization. One of the methods includes receiving data that identifies assets, attributes for each of the assets, and respective relevance ratings for the assets, receiving threat data that identifies vulnerabilities of particular attributes, determining vulnerability trends for the particular attributes, determining whether an attribute is one of the particular attributes identified in the threat data, updating the relevance ratings of the attribute using the vulnerability trends for the attribute, for each of two or more vulnerabilities identified in the threat data: determining the particular attributes affected by the vulnerability, and determining a score for the vulnerability using the respective relevance ratings for the particular attributes affected by the vulnerability, generating a ranking of the vulnerabilities using the corresponding scores, and generating instructions for presentation of a user interface that identifies each of the vulnerabilities.

A device may obtain information regarding a security situation of a set of computing resources associated with a cloud-based platform. The information may be related to an ongoing security threat or a potential security threat. The information may be obtained utilizing one or more internet security resources. The device may determine a threat assessment level, of a set of threat assessment levels, for the security situation based on the information regarding the security situation. The information regarding the security situation may satisfy a set of threshold criteria for the threat assessment level. The device may perform one or more response actions associated with the threat assessment level based on the security situation. The one or more response actions may include providing an alert notification regarding the security situation that identifies the threat assessment level.

Cloud computing technical components are provisioned into many different service platforms provided by many different service providers. Once provisioned, it is often the case that actions need to be performed against the technical components. An action execute architecture executes the actions across a wide range of disparate service providers hosting both public and private target hosting platforms, while also providing a much more flexible and dynamic environment for implementing and deploying those actions.

Agency: European Commission | Branch: H2020 | Program: IA | Phase: LCE-02-2016 | Award Amount: 22.78M | Year: 2017

Five DSOs (CEZ distribuce, ERDF, EON, Enexis, Avacon) associated with power system manufacturers, electricity retailers and power system experts, propose a set of six demonstrations for 12 to 24 months. Within three years, they aim at validating the enabling role of DSOs in calling for flexibility sources according to local, time-varying merit orders. Demonstrations are designed to run 18 separate use cases involving one or several of the levers increasing the local energy system flexibility: energy storage technologies (electricity, heat, cold), demand response schemes with two coupling of networks (electricity and gas, electricity and heat/cold), the integration of grid users owning electric vehicles, and the further automation of grid operations including contributions of micro-grids. The use cases are clustered into three groups. Three use cases in Sweden and the Czech Republic address the enhancement of the distribution network flexibility itself. Five use cases in France, Germany and Sweden demonstrate the role of IT solutions to increase drastically the speed of automation of the distribution networks, which can then make the best use of either local single or aggregated flexibilities. Ten use cases in Czech Republic, France, The Netherlands and Sweden combine an increased network automation and an increased level of aggregation to validate the plausibility of local flexibility markets where both distributed generation and controllable loads can be valued. Replicability of the results is studied by the DSOs and industry with an in-depth analysis of the interchangeability and interoperability of the tested critical technology components. Dissemination targeting the European DSOs and all the stakeholders of the electricity value chain will be addressed by deployment roadmaps for the most promising use cases, thus nourishing the preparation of the practical implementation of the future electricity market design, the draft of which is expected by end of 2016.

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