Data Visualization Sample Clauses

Data Visualization. The buildingeye/CivicCentral application functions in a logical workflow and visualization for the general public, agency staff and officials. Each marker on the application is interactive, when clicked an information panel appears with details and links to back office record details. The interface is pre-populated with markers for the relevant module, noting where records exist. Data is visualized on a web application (compatible for mobile and tablet) with the ability to filter applications by keyword, type, timeframe and status. A user may need to be registered to see certain features and attributes. The map icons are dynamic and produce an information window with metadata on the application/project. The workflow should be clear for users to understand where in the process the project is.

Data Visualization. ECO-COUNTER offers a data visualization service via web access to the ECO-VISIO Platform. The ECO-VISIO Platform is a visualization and analysis software for the daily Data transmitted by the Client’s ECO- COUNTER Products. This Service includes administrator access to the ECO-VISIO Platform for the Client. Using this administrator access, the Client can create and manage access accounts for its other users. The Client has full responsibility over such access, including with respect to the secure use of the Data and the ECO-VISIO Platform. ECO-COUNTER cannot be held responsible in the event of improper handling of the Data by the Client’s administrator or one of the Client’s users. Access to the Platform is for a limited term, corresponding to the term of the Contract and of the Services purchased by the Client. In case of non-renewal of the Services, ECO-COUNTER will deactivate the Client’s access to the Platform (both the administrator account and all user accounts). To allow the Client to resubscribe without losing its user accounts, ECO-COUNTER will not delete any Client accounts until six (6) months after the termination of the Services.

Data Visualization. Error analysis will be a key goal of this paper, but data visualization is also important. Data visualization is an important element in understanding the items at hand as well as bringing meaningful conclusions to a broader audience. This is especially true when the heart of the information lies in 50,000 plus entry matrix; it is extremely hard to look at a large matrix and intuitively understand that entries are right or wrong. The post processing of the solution in both FEM1D and FreeFEM++ provide visual synthesis of the data output. However, since the original B & S problem requires manipulation in order to pose the “physical” (financial) space to the mathematical (solvable) space, visualization will require some additional steps to unwind this and present the solution back in the intuitive financial space. The

Data Visualization. Data visualization findings can be clustered in two (2) classes. The first one contains a set of general characteristics and functionalities that should be provided by a data visualization solution. The second one is a set of information categories that should be provided by a data visualization solution. General characteristics and functionalities of data visualization DV_REQ_01 Access from different devices: Data Visualization part, should be accessible from different entry points or at least should support a minimum set of devices which are mainly used in industry. DV_REQ_02 Adapted to the specific audiences: By knowing who is going to use the dashboards, it helps to choose the best way to present the data according to their specific needs. DV_REQ_03 Network connectivity: Data visualisation requests data from CPS’s and serves them as dashboards to final user’s devices, through a wired or wireless network. DV_REQ_04 Close the P-D-P loop with the human involvement: The final decision and action in traditional maintenance process belongs to human. This happens as well, into the digital one. The scope of data visualization is to support the interaction with human and to close the physical – digital – physical loop. DV_REQ_05 Help the staff to make decisions: Most of the decisions for the maintenance actions are taken from technician staff. Data visualization should support these decisions by providing the necessary information and recommendations in real and non-real time. DV_REQ_06 Provide information to different users: Based on WP 2 analysis, there are two maintenance loops, one to the machinery level and another one to the factory level. Moreover, many different users are involved to the maintenance process such as technician, maintenance engineer, etc., and the information they need may differs. Data visualization should provide the right information to the right level. DV_REQ_07 Provide to the users, information about tasks to be completed: A predictive maintenance system produces ticketing and task assignments for scheduled and unscheduled jobs. Data visualization is recommended to support mechanism for visualizing these tickets and assignments to the specific staff. DV_REQ_08 Give to the technician guides or recommendations about maintenance actions: A predictive maintenance system produces, through analytics, a set of recommendations. Data visualization is recommended to support a mechanism for visualizing these recommendations to the specific staf...

Data Visualization. Bellow we analyse predictive maintenance solutions or approaches which use visualization techniques for the human involvement to the maintenance loop.