Abstract: Various embodiments are directed to managing annotations over a network for visualizations. An annotation engine enables users to associate a data object value with any number of notes, comments, videos, graphics, pictures, audio, references, links, or any other information A visualization engine generates visualizations that include annotation identifiers when the visualizations include data object values that are associated with annotations and the type of visualization is approved for use with the annotations.

Abstract: Embodiments are directed toward unified modeling of technology towers. Template information that corresponds to a technology tower may be selected by a user such that the template information identifies cost items, cost pools, and cost drivers. Cost information corresponding to the cost items may be determined from a dataset and mapped to the cost pools based on the template information. Cost driver information for the cost drivers may be determined from the information that was included in the dataset. A technology tower may be generated based on the cost pools and the cost drivers such that the cost pools and cost drivers correspond to the cost items. The technology tower may be provided to an application for generating one or more financial models. Key performance indicators for cost items may be generated based on the cost pools and the cost drivers.

Abstract: Embodiments are directed towards infrastructure benchmarking based on dynamic cost modeling. Raw customer models that represent a customer's business system may be ingested into a benchmarking system and mapped to customer models based on one or more standard models. Benchmarking information that may be generated from one or more community models that are one or more customer models of other customers that may be available to the benchmarking application. Additional third-party information, such as, industry survey information may also be used for generating benchmarking information. The benchmarking information may be used to generate reports that include comparisons between one or more customer models and the one or more community models. The benchmarking information may be continuously updated based on changes to customer models and/or data, community models and/or data, external information.

Abstract: Embodiments are directed towards for models for visualizing resource allocation. A plurality of data models may be provided to a benchmarking engine. The benchmarking engine may be employed to provide a plurality of benchmark models based on the plurality of data models. If an organization is selected, additional actions may be performed including: providing one or more of the plurality of benchmark models based on a data model that corresponds to the organization and one or more model objects of the data model; and, employing a visualization engine to provide a visualization that compares one or more resource allocations associated with the one or more model objects with the one or more benchmark models, such that the visualization is displayed on a hardware display for viewing by a user.

Abstract: Embodiments are directed towards providing dynamic recommendations of reserving information technology resources over time that may be visually displayed over that time. In one embodiment, the recommendations may be determined based on an analysis of actual usage data obtained over a prior time period and used to predict future resource demands. The subject innovations enable a user to dynamically perform various ‘what-if’ analysis to determine optimum purchase times, and configurations. In some embodiments, the user is further provided information about currently purchased resource under-utilizations to enable the user to redistribute work, release resources, or take other actions directed towards improving management of their IT budget.

Abstract: Embodiments are directed towards for managing data models, including resource allocation forecasting. A main data model may be provided. A delta ratio value based on a difference between modified cloned resource values and their corresponding original resource values in the main data model may be provided. Line items from the cloned data model associated with the one or more modified cloned resource values may be provided. Each of the cloned line items may be modified based on the delta ratio value. The modified cloned line items may be stored in the cloned data model. Reports including report information based on the cloned data model may be provided. The report information may indicate changes that were made to one or more other cloned resource values based on the modifications to the one or more cloned resource values.

Abstract: Embodiments are directed to identifying allocation discrepancies. Data models and Benchmark models may be provided to an analysis engine. Discrepancy models may be provided to the analysis engine, such that each discrepancy model may be arranged to include one or more rules. The analysis engine may be employed to search for discrepancies in the data models based on the discrepancy models and the benchmark models. If discrepancies may be identified by the analysis engine, one or more notifications may be provided to one or more users. Also, the discrepancy models may be modified based on subsequent feedback provided by the one or more users. Correlations in the resource allocation values may be identified based on machine learning that includes one or more of linear regression, deep learning neural networks, or the like. And, additional discrepancy models may be provided based on the identified correlations.

Abstract: Embodiments are directed to managing data models for managing resource allocation. A data model portion may be provided. Allocation information based on resource allocations associated with pass-through objects that may be included in the data model portion. A memory buffer may be configured to include allocation ratio information and fixed resource value information. A reciprocal model based on the memory buffer and the data model portion may be provided. Providing the reciprocal model may include providing an effective resource value engine based on solving a linear system corresponding to the allocation ratio information and the fixed resource value information. If resource information for a pass-through object associated with the reciprocal model may be requested, the reciprocal model may be employed to provide the resource information to the data model.

Abstract: Various embodiments are directed towards allocating costs for a plurality of cost objects. In at least one of the various embodiments, a source object and a target object in a data model may be determined such that an allocation rule is used to define one or more cost allocations where the costs flows from the source object to the target object. Allocation rules that are part of a recursive allocation rule may be executed on the source object and the target object until a terminal condition is met. The cost value that corresponds to the source object may be modified based on the allocation rule and the generated cost value. This process may continue until a terminal condition is met. After the terminal condition has been met final costs value corresponding to the target object and the source object may be generated and displayed.

Abstract: Embodiments are directed towards allocating resources in a business system. A data model that includes a plurality data objects may be generated, such that each data object includes a plurality of data object line items. Allocation rules that allocate resources between the data objects may be generated. The allocation rules may be employed to identify key features of data objects, such that the key features are used by the allocation rules to allocate resources. If key features are identified, grouped objects that separately correspond to the data objects that include key features may be generated. Also, grouped object line items may be generated for each of the grouped objects based on each distinct value of the key features, such that the data object line items are collapsed into grouped object line items based on the distinct values of the key features.

Abstract: Embodiments are directed towards managing changes to data. A modeling engine may provide a data model based on objects comprised of one or more versions of one or more properties. Each version of the properties may be associated with separate validity ranges over time during which each version is valid. A report for visualizing the data model at a point-in-time may be provided. One or more report values may be provided based on each version of the properties that have a validity range that includes the point-in-time. The report may be displayed to provide provides one or more visualizations based on the one or more report values.

Abstract: Various embodiments are directed towards including heritage information when allocating costs for a plurality of cost objects. A target object, a source object and heritage objects may be determined from a data model. At least one line item in the source object may be generated by allocating costs from the heritage objects with the generated source object line items corresponding to a line item from a heritage object. At least one line item in the target object may be generated based on allocating costs from the source object. And, at least one generated target object line item may be based on at least one source object line item and its corresponding heritage object line item. A final cost value for the target object may be generated based on a sum of each target object line item and displayed in the data model.

Abstract: Embodiments are directed towards intermediate resource allocation tracking in data models. A data model that includes data objects traversed on a path through the data model that follows allocation rules that are included in the data model. If an aspect object is encountered during the traversal, resources from line items of a source data object are associated with line items of the aspect object based on the traversal path. The resources associated with the line items of the aspect object are also available for allocation to other data objects. If a data object, rather than an aspect object, may be encountered during the traversal, resources from line items of a source data object may be allocated to line items of the data object based on another of the allocation rules, the resources allocated to the line items of the data object are unavailable for further allocation.

Abstract: Embodiments are directed towards generating reports for visualizing data models. A RACI engine may obtain RACI information for a user. A report engine may provide metadata associated with a report for visualizing the data model. The report engine may be arranged to provide anchor points in the data model based on the metadata, the RACI information. The report engine may be arranged to provide a report template based on the anchor points, the RACI information, and the metadata such that the report template includes references to data objects selected automatically based on the metadata and the anchor points. The report engine may display a report based on the report template that provides visualizations based on values associated with the data objects.

Abstract: Embodiments are directed to modeling resource allocations. A data model may be provided to an activity modeling engine. The activity modeling engine may be employed to provide an activity model based on the data model, such that the activity model may include activity objects that each may be associated with bills-of-materials (BOMs). Resource allocations from source activity objects to target activity objects may be provided based on the BOMs, such that the resource allocations may be modeled using unit values defined by the BOMs. A report visualizing the activity model may be provided and presented on a hardware display.

Abstract: Embodiments are directed towards normalizing ingested data sets based on fuzzy comparisons to known data sets. Raw data sets that each include raw records may be provided to an ingestion engine. Ingestion rules and known data sets may be provided based on the raw records. The ingestion engine may be employed to iteratively execute the ingestion rules. A comparison of the raw records to the known data sets may be performed. Contents of the raw records may be transformed into model record values and stored in model records. A score value that indicates a confidence level that the model records are correct may be provided. An association of the one or more ingestion rules used to transform the raw record contents into the model record values for each of the one or more model records may be added to a data model.

Abstract: Embodiments are directed towards managing data models using version control for resource allocation modelling. A main data model that includes change records, resource values, model objects, or the like, may be provided. Also, user workspaces associated with the main data model may be provided. The user workspaces may include a workspace data model that is based on the main data model and its change records. Model objects that may be part of the user workspace may be modified by a user where each modification made by the user may be employed to provide workspace change records. If a user finishes modifying the workspace data model, the one or more workspace change records may be used to execute the merge of the modified model objects with the main data model.

Abstract: Embodiments are directed towards visualizing the flow of resources in an allocation model. A visualization model may be selected based on an underlying allocation model. The visualization model may include visualization model items. Allocation model items in the allocation model may be mapped to the visualization model items included in the visualization model. If a visualization model item is selected, the source visualization model items that provide resources to the selected visualization model item may be determined. And, the target visualization model items that receive resources from the selected visualization model item may be determined. Input flow lines that start from the source visualization model items and end at the selected visualization model item may be generated and displayed. And, output flow lines that start from the selected visualization model item and end at the target visualization model items may be generated and displayed.

Abstract: Various embodiments are directed towards a platform for rapid development of applications. A data processing application may be generated by applying templates, work streams, change entries, and user modifications to generate data processing applications. In at least one of the various embodiments, the applications may be generated based project initial instructions and by receiving and processing example data in combination with templates, change entries, and work streams. Further, in at least one of the various embodiments, modifications made to the generated applications may be preserved as change entries associated with a work stream. In at least one of the various embodiments, separate applications may be generated based on the work streams and/or change streams of other applications. In at least one of the various embodiments, if a generated application may be modified a new revision number may be associated with the modified version of the application.

Abstract: Embodiments are directed towards managing data storage and queries within a database system using global dictionaries with universal primitives (UNIPs) to represent non-numeric data within a mixed numeric/non-numeric environment. Common data types are managed within a same global dictionary through dictionaries that are globally used within the database system. At least non-numeric data within mixed data fields may be stored using a UNIP to identify the stored non-numeric data. The UNIP may take advantage of the IEEE-754 standard for floating point data representation by setting a first field within the UNIP to 0x7ff (HEX) to indicate that the data is non-numeric (NaN) and using remaining bits to store typed data, such as a date or unique indirect reference (e.g. a sequence number or file offset to larger piece of data). The UNIP may then replace the data within the database and be used during operations performed on the data.