Insights. 3.1 The insights that will be generated from the environmental data described above include insights relating to:
3.1.1 Damp and mould;
3.1.2 Heat loss;
3.1.3 Indoor air quality;
3.1.4 Cold homes;
3.1.5 Excess heat;
3.1.6 Draught;
3.1.7 Dust Mite allergen; and
Insights. “Insights” is a tool in the Financial Tools available through the SNB Mobile App that provides personalized insights, including information about your spending, deposits, subscriptions, and upcoming recurring payments, in a social-media- style feed to help you monitor and manage your finances. To make Insights more useful to you, you may provide feedback in the tool about which types of Insights you would like to see more or less of. You can delete Insights from your feed at any time within the SNB Mobile App. Insight content is based on information from your Accounts and other accounts you aggregate using the Financial Tools. You should not rely on Insights for legal, tax, financial or other professional advice. Insights are not necessarily provided in real-time.
Insights. On Time Performance PRODUCT NUMBER OF VEHICLES ANNUAL COST PER VEHICLE TOTAL COST PER YEAR Connect with Passengers Connect with Staff Connect with Vehicles Connect with Partners TOTAL ANNUAL COST $101,396.94
Insights. 9.1 To the extent offered and technically feasible you can access account information on your bank accounts in the Xxxx App through an AISP working with us. The account information will be provided by the respective AISP. For this purpose, you will be required to enter into one or more separate agreements with our nominated AISPs. We are not a party to the AISP agreements but may act as agent of such AISPs. Further information will be displayed in the Xxxx App. Where there is any conflict between the terms of any agreement between you, us, and an AISP, the terms of this Agreement will prevail in relation to the provision of the Xxxx Services.
Insights. As initially planned in deliverable D5.2, this PMM experiment has been designed to identify relevant technical feasibility aspects and business implications of distribution of media in smart city areas. The actual experimentation occurred in Barcelona has allowed us to derive useful insights for the main business actors of interest, i.e. Media/VoD service provider and Media/VoD technology provider but it allowed also to derive useful insights for the infrastructure owner/operator, as detailed in the following. From a technical perspective, the PMM experiment has emulated the behaviour of a group of people sharing a media streaming service where personal contents are stored and willing to consume these media while moving from a central place (home) into a specific area of the city. To emulate the localisation of the group of people in FLAME coverage, we configured our scale-out function to activate and connect all the placed Origin Server replicas in the three remaining edge cabinets. The implementation in a wider geographical area, e.g. in the city of Barcelona, would require using localisation functions to activate only the server replicas available in the district where users are identified and may require coverage along their walking paths in the area. The dimensioning of the experiment documented in the previous sections shows that the deployed media SF endpoint could not support the coexistence of 3 groups of 4-5 people each, with a high number of blocked streams, due to transcoding on the target media server (origin or replica). The analysis of results shows that the main cause for this behaviour can be the limited amount of computations resources made available for PMM SFEs in the Barcelona platform (2 virtual CPUs and 4GB RAM per media server). In fact, PMM media servers are generally dimensioned with 4-6 CPUs and 8-12 RAM to serve 4-10 users. Moreover, the use of different devices with different form factors (tablets, smartphones with and without FullHD, various form factors for the screens) caused a number of parallel transcoding activated by the media application to adapt the played content to the actual device capabilities. Despite the creation of pre-transcoded versions of the same contents which was aimed to avoid the transcoding event and make use of direct streaming, we experimented recurrent degradation of the overall QoE both in terms of objective parameters and if subjective user “acceptance” of the media service quality when run on FLAM...
Insights. Build reports to make faster, data-driven decisions. All the data you need, without the noise. Features You’ll Love Create the client intake form that fits each individual practice needs and specialization.
Insights. Dashboards This feature enables contact centre managers to gather and visualise all their KPIs in one place. You can build your own dashboards incorporating any of the traffic, queue and agent performance data available within your Puzzel solution.
Insights. Note: these are preliminary insights based on initial results. This section will be expanded upon when the full results are available.
Insights. Numbrs uses one of the most advanced AI technologies for analysing financial data and helps you manage your finances more easily, quickly and smarter. The AI function analyses all of your transactions. The information is provided “as is”, without warranty of any kind, whether express or implied. Numbrs assumes no responsibility for errors or omissions and will not be liable for any direct, or indirect losses.
Insights. Our evaluation indicates that the FLAME platform is capable of delivering 3D assets quickly and efficiently for video game applications. For larger number of players, the load on the server remained largely the same as for fewer players, which can be accredited to the Opportunistic Multicasting capabilities of the FLAME platform. To benefit from these capabilities, the underlying client code has to be tuned in a way that lets all clients request the same asset simultaneously. Players who were in the game since it started, never observed any delay in asset loading. Players who join mid-game, which may happen in multiplayer games, put an over-proportional high stress on our test system. These were also the only players who observed a short delay while the 3D models were continuously loaded into the game. The main reason for this is that these players have to download all currently used 3D assets as soon as they join the game. This process takes time and the servers cannot profit from the Opportunistic Multicasting, as only one player is requesting these assets at that moment in time. However, as the content was served from the edge of the network, the problem was mitigated. This drawback is not a limitation of the FLAME platform but of the game design and implementation. Finally, we conclude that the FLAME platform provides both high QoE and QoS as measured during our trials. Asset loading went smoothly and efficiently, while the gameplay remained uninhibited by the FLAME platform. Players were immersed in the virtual AR landscape and mostly unaware of 3D assets loading in the background. Opportunistic Multicasting helped at keeping the network load on our server at a minimum and request processing times were low, making the FLAME platform well-suited for urban multiplayer games. While this prototype of Gnome Trader targets the area of Millennium Square in Bristol, UK, and up to 10 players, future efforts could focus on exploring how similar games targeting larger areas, for instance, an entire city, perform on the FLAME platform. Our results indicate that such city-wide multiplayer games may be well-accommodated by the FLAME platform because of its scalability.
