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. 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.
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 underlying data model used by the system must be tested to conform to the standard of a so-called third generation system. This means that the data model must be capable of the following:
i. A Point-of-Connection must be supported which is independent from a Location, Meter and
ii. The tariff must not be connected to a Meter or a Consumer, but must rest with the Point-of- Connection.
iii. The data model must allow for the definition of hierarchical Nodes to simulate a distribution network.
iv. The data model must allow for WGS-84 GPS coordinate definition with all locations.
v. The data model must accommodate, for enhanced management purposes, possible additional resources like water, gas and other utilities.
vi. The data model must accommodate meter reading for reading prepaid meters, and reconciling meter consumption with sales.
Data Model. We assume that the empirical xxxx Xx(s) can be decomposed into two components: a spatial component Mj(s) and a noise component εj(s): Bj(s) = Mj(s)+ εj(s); j = 1,...,Q (2.1) ε,j ε,j where {εj(s)} is a Gaussian white noise with zero mean and variance σ2 , and independent from the spatial component {Mj(s)}. Additionally, the noise component {εj(s)} is assumed to be independent from {εk(s)}, for k ≠ j. Thus, conditionally on the hidden spatial process {Mj(s)}, the observed xxxx Xx(s) has a Gaussian distribution with mean Mj(s), and variance σ2 that represents the data model level.
Data Model. The idea is that in the evaluation of the systematic bias B(s) the local spatio-temporal effects should be filtered out. To model the deviation, we assume that the observed deviation Dt(s) can be decomposed into two components: Dt(s) = Mt(s)+ t(s) (3.2) t where Mt(s) is a spatio-temporal Gaussian random field and t(s) is a temporally and spatially uncorrelated zero mean Gaussian noise with variance s 2. Note that the model is allowed to take into account for the heterogeneity in time. We assume that the noise component t(s) is independent of the deviation process Mt(s). In practice, we convey into the process Mt(s) all smoothed spatio- temporal components that actually are blurred by the noise term. We further assume that the observed deviation Dt(s) is conditionally independent in time given Mt(s). Such assumptions lead to the data model in the form where [A] denotes the generic notation for the probability distribution of the random quantity A. Accordingly [A|B] is the conditional distribution given B.
Data Model. 1- What is the meter data model (DLMS/1107 or other)
Data Model j ≤ ≤ ≤ ≤
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. The CODI Data Model, pursuant to “Exhibit 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. 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).
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