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Andreas Doms

Katja Pfeifer

Konrad Voigt

Petko Ivanov

Embodiments facilitate selection and assignment of a known user model, based upon input comprising table images of original data. A table engine receives the image and performs pre-processing (e.g., rasterization, Optical Character Recognition, coordinate representation) thereupon to identify image entities. After filtering original numerical data, a similarity (e.g., a distance) is calculated between an image entity and a dimension member of the known user model. Based upon this similarity, the table engine selects and assigns the known user model to the incoming tables images, generating a file representing table columns and rows. This file is received at the UI of an analytics platform, which in turn populates the model with data of the user (rather than the original data) via an API. Embodiments may be particularly valuable in allowing a user to rapidly generate multi-dimensional tables comprising their own data, based upon raw table images received from an external party.

Multi-dimensional table reproduction from image

Constraint association, constraint persistence, and constraint execution is provided herein. Constraints can be associated with one or more cells in a datacube. The constraints can be stored or persisted with the datacube or in association with the datacube. The constraints can include rules, limitations, or functions determining the set or scope of possible values for the associated cells. The constraints can be applied to the cells when first associated, or when a constraint trigger is received or performed, such as a change to the value of an associated cell. Constraint execution can cascade to additional cells not directly associated with the constraint, and can invoke additional constraints in the cascade. Constraints can include configured constraints, associated to cells by a user via a constraints menu. Constraints can include data model constraints based on a data model for the datacube. Constraints can include default constraints applied during constraint execution.

Persistent and configurable multidimensional data constraints

A technology for displaying formula dependencies of multi-dimensional data in a visual representation is provided. In accordance with one aspect, a visual representation of a multi-dimensional data is provided based on metadata of a modeled data set. The metadata includes information of a data model comprising dimensions, hierarchies of dimension members, and formulas for deriving the dimension members. The formula dependencies are determined for members of a dimension based on the metadata. A user selection of a dimension member in the visual representation is received and the framework displays a formula dependency of the selected dimension member on the visual representation using one or more visual markers. The visual markers identify one or more corresponding dimension members from which the selected dimension member is derived and represent a formula used for deriving the selected dimension member.

Visualizing dependencies of multi-dimensional data

A system may include display of a plurality of values, each of the plurality of values associated with a respective combination of one of a first set of members of a first dimension and one of a second set of members of a second dimension, detection of a user selection of one or more of the displayed plurality of values, determination of a first one or more members of the first set of members and a second one or more members of the second set of members associated with the selected one or more values, and filtering of the displayed values based on the determined first one or more members and the second one or more members.

Direct cube filtering

A method for selecting thresholds for a data variable or key performance indicator (KPI) includes processing, by a computer, an actual data set for the data variable or KPI and displaying a perspective view representing the actual data for the data variable or KPI in an interactive threshold editor or selection dialog on a computer-user interface. Processing the actual data set for the data variable or KPI includes statistically curve fitting the actual data set to a probability distribution function and determining a set of pre-selected thresholds based on the parameters obtained by fitting the actual data set to the probability distribution function. The method further involves presenting one or more user-activable UI elements that are configured to receive user acceptance, rejection or adjustment of the pre-selected thresholds as thresholds for the data variable or KPI.

Intelligent threshold editor

A computer-implemented method includes receiving an offer of a flexible demand response event from a customer of a utility company, and determining a price of the offer in context of a load-supply balancing operation of the utility company.

Flexible energy use offers

Technologies are provided for validating application models within a database environment. For example, an application model can be provided to a database environment and validated against a meta-schema and/or an application schema stored within the database environment. Successful validation can be required before the application model is allowed to run within the database environment (e.g., before the application model can be stored within the database environment).

Schema-based application model validation in a database

A code generating framework generates code for a model Application Program Interface (API). The framework comprises three components: an API code generator, a serialization code generator, and a deserialization code generator. The API code generator generates code for a model API. This model API produces a first model instance version in a first language. Code from the serialization code generator converts the model instance into a second version in a different language accessible to other applications (e.g., graphical modeling editors). Code from the deserialization code generator converts the second version of the model instance back into the original language. In a particular embodiment, the code generation framework generates JavaScript/XSJS APIs for manipulating model instances compatible with the Eclipse Modeling Framework (EMF). Serialization and deserialization code generated by the framework, converts the JavaScript/XSJS model instance into XMI recognized by other applications built on top of EMF, and back again into JavaScript/XSJS.

Code generation framework for application program interface for model

An electric socket can include a visual indicator capable of displaying time-series forecast information that forecasts a property or attribute related to the power being sourced from an energy provider. The information being displayed on the visual indicator can help an energy consumer decide whether to use an appliance now by plugging it into the electric socket or wait until some optimal time in the future to plug the appliance in.

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Dresden, Germany

Andreas Doms