Data Model Sample Clauses

Data Model. Figure 22 illustrates how the above data elements interrelate. Xxxxxx describe one to many relationships, where the arrow points from “many” to “one”. A provisioning process owner (entity element) can have many provisioning process elements. A provisioning process element can have many key elements. A provisioning process element can have many audit elements. An accessor entity element can also have many audit elements. Figure 22: Data Model To get information on a private key from the private key’s public key one would hash the public key to create the address, look up the address on the Blockchain, look up the process element that created the address, and finally look up the latest audit element of the process. One could also obtain information on the accessor and process owner if desired.
AutoNDA by SimpleDocs
Data Model. ‌ ODV introduces a new data model and storage format, suitable for marine and other environmental profile, trajectory and time-series data. Individual stations in a data collection are described by a set of metadata. There is a minimum set of mandatory metadata, including cruise and station names, longitude, latitude as well as date and time. In addition, users may specify an arbitrary number of optional string or numeric metadata. Except for longitude and latitude, all other metadata values may be missing. As for metadata variables, the number of data variables (e.g., the variables holding the actual data) in ODV collections is unlimited, and the value types may be numeric or string. ODV supports a variety of numeric metadata and data variable value types ranging between single-byte integers and 8-byte double-precision real numbers. Quality flag values of metadata and data variables can be encoded using one of the supported 16 popular quality flag schemes in the community. ODV lets you subset and filter stations as well as samples, and provides separate sets of station and sample selection criteria. Every data window now uses its own sample filter, and filtering can be by quality flags and/or value ranges (numeric ranges or string wildcard expressions). Newly created ODV collections use the new data model and file storage format. ODV data collections are platform independent, and may be transferred between all supported systems.‌
Data Model. The ARROW DataCentre, at the current stage, has the main task of managing the whole workflow sending and retrieving data to/from the data providers. All of these data are what the system needs to store, in addition to other data that result from some specific business activity (e.g. publishing status). The resulted data model can be better explained through the following figure.
Data Model. Figure 8 shows the Entity-­Relationship (ER) model of the web-­based system. This ER model is the result of analysing the main concepts employed in WP2 deliverables, as well as the way the information is presented in them. This technical-­oriented ER model has also been agreed with CARESS WP2 partners, that are domain experts in Health Homecare.
Data Model j ≤ ≤ ≤ ≤ In the proposed recommender system, we assume there are m items, from which an item is selected for recommendation. Each item ti (1 i m) has n at- tributes that describe the item’s features. We also as- sume that there are K users in the group. User uk (1 x X) will input their requirements r(uk) for each attribute j. Item ti is assumed to have an evalua- tion function eval(ti) for each attribute j, which takes each user’s requirement value for attribute j, r(uk), as its parameter and returns an evaluation score about the user’s satisfaction of item t j regarding attribute
Data Model. ‌ To enable samples and data to be searched in a comparable way, the first development step was designing an extensible data model, that covers all three key components of biobanks: (a) biological material and associated physical storage facilities, (b) data and associated data storage facilities, and (c) expertise of the biobankers. The core of the data model for the Directory 2.0 relies on to MIABIS 2.0,27 a standard data model for biobanking, which is evolution of the previously published MIABIS model.28 As shown in Figure 1, this includes the following basic entities: • biobanks are the institutional units hosting collections of samples and data, as well as providing expertise and other services to their users. This entity does not contain directly any attributes related to the samples or data, which are implemented via links to the collections that are available in the given biobank. • collections are containers for sample sets and/or data sets, with support for recursive creation of sub-collections (of arbitrary finite depth); here properties of the samples and data can be described in aggregated form such as sample counts, diseases, material types, data types, gender, etc.; • networks of biobanks (not defined in the MIABIS 2.0), which may include either whole biobanks or even individual collections inside the biobanks; • auxiliary contact information contact information attached to biobanks, collections and networks needed to get access to samples or data (which is defined centrally to minimize redundancy in the information model). The data model has been defined in a modular way such that auxiliary classes can be added to suit the needs of biobank (sub)communities, such as to describe clinical, population, research study based, non- human, and standalone collections. Particularly clinical collections are used to enforce existence of at- tributes describing available diagnoses (which is optional for other types), as it is among the most common search criteria.26 Standalone collections are used in the countries with legal requirements on institutional- ized biobanks, if there are some collections that do not meet these requirements (yet).
Data Model. 1-What is the meter data model (DLMS/1107 or other)
AutoNDA by SimpleDocs
Data Model. The CODI Data Model, pursuant toExhibit B” of this Agreement, relies heavily on the CHORDS VDW data model but is not identical to the CHORDS VDW data model. CODI includes ancillary Data tables specific to obesity-specific interventions; the CODI ancillary tables are not part of the CHORDS data model. CHORDS data quality activities do not include monitoring Data quality in CODI-specific Data Model elements. CODI Data Partners populate the CODI Data model based on availability and feasibility; thus the completeness of Data within the CODI Data model varies across Data Partners.
Data Model. The VDC data model can be viewed as split in two parts (for readability purposes), one comprising the physical resources and one including all the virtual instances of the computing, storage and network functions related to a given customer/user/tenatnt. The physical part of the VDC is described in Figure 31. Figure 31 Data model of the physical resources in VDC VDC is the master root entry and includes a number of VDC Zones, typically related to a geographical location of the physical resources (e.g. a data centre in Rome, London, Copenhagen, etc.). Within each VDC Zone there are three major types of physical resources:  PHY Computing groups: the physical servers allocated for VDC purposes at a given VDC zone, each comprising a number of PHY CPU and PHY RAM to operate Virtual Machine instances and execute workloads.  PHY Storage groups: the physical devices and disks used to implement the storage areas of the different VMs. Different technologies and characteristics of the storage devices exist to respond to different application requirements in in terms of I/O operations, speed, volume size, etc. PHY Network groups: the physical assets used to implement the network and security services in the VDC, and in particular physical LAN segments (PHY LAN), physical gateways towards the Public Internet (PHY GW), physical Load Balancers (PHY LB) and physical firewalls for the typical firewalling and NAT-ting tasks (PHY FW). The virtual part of the VDC data model is described in Figure 32. Figure 32 Data model of the virtual resources in VDC Also in this case the VDC element is the master root entry and includes a number of VDC Zones and a number of VDC customers or users. Each VDC customer instantiates a number of Virtual Machines (VM) and Virtual Networks (vNET) to implement his IaaS service. All VM and vNETs live across VDC zones and VMs in particular belong to Affinity Groups that regulate the user/customer preference on how to allocate and select virtual resources on top of physical resources. Affinity Groups contain constraints for the orchestrator allocation functions. Each VM is instantiated based on a selected VM template which corresponds to a predefined application/performance profile as well as price reference. Under each VM a number of virtual CPUs (vCPU), virtual RAM (vRAM) and virtual storage volumes (vStorage) are attached, as well as a number of virtual network interfaces (vNET-IF) used to interconnect the different VMs within the customer IaaS s...
Data Model. Each data model specifies how a certain set of resources is represented (organized). There are several data models that the orchestrator must coordinate for the Network Virtualization use case.
Time is Money Join Law Insider Premium to draft better contracts faster.