Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically generating a partially-editable dataset copy. One example method includes receiving an edit request from a user to edit a data set. A filter for the data set is determined, based on a context of the edit request. The filter is used to generate a partially-editable copy of the data set that includes the editable portion of the data set. An exposed view of the data set is generated that exposes the partially-editable copy of the data set and a non-editable portion of the data set. The exposed view of the data set is provided to the user and a change to a data item in the editable portion of the data set is received. The data item in the partially-editable copy of the data set is updated based on the change.

Abstract: The present disclosure involves systems, software, and computer implemented methods for providing quality-driven processing of out-of-order data streams based on a configurable quality metric. In one example, the method includes identifying a configurable quality metric defined by a user or application for executing continuous queries over a sliding window, the metric specifying a user requirement for the quality of the continuous query result. A quality threshold metric is calculated based on the configurable quality metric, the configurable quality metric associated with a size of an adaptation buffer for incoming tuples of the query. In response to receiving a signal indicating a new query result, a sliding window prior to a current time is identified. A runtime quality metric associated with the incoming tuples for the sliding window is measured, and the adaptation buffer is modified based on a difference between the quality threshold metric and the measured runtime quality metric.

Abstract: The present disclosure involves systems, software, and computer implemented methods for optimizing continuous queries for hybrid execution over a stream processing engine and an in-memory database. In one example, a method may include identifying a continuous query executed at a stream processing engine, the continuous query including a plurality of operators. An optimal plan for execution of the identified continuous query at the stream processing engine is determined. For each of the plurality of operators in the determined optimal plan, an optimized placement decision for executing a particular operator in the stream processing engine or at a database system is determined. An optimized continuous query is generated from the identified continuous query based on the determined optimal placement decisions for each of the plurality of operators in the determined optimal plan. The optimized continuous query is then executed at the stream processing engine and the database system.

Abstract: Allocation within a database environment is performed by processing a sequence of steps, each of which is described by an allocation processing block. The allocation processing block receives three inputs (input tables or input view) and computes a result. Related apparatus, systems, techniques and articles are also described.

Abstract: The present disclosure involves systems, software, and computer implemented methods for providing quality-driven processing of out-of-order data streams based on a configurable quality metric. In one example, the method includes identifying a configurable quality metric defined by a user or application for executing continuous queries over a sliding window, the metric specifying a user requirement for the quality of the continuous query result. A quality threshold metric is calculated based on the configurable quality metric, the configurable quality metric associated with a size of an adaptation buffer for incoming tuples of the query. In response to receiving a signal indicating a new query result, a sliding window prior to a current time is identified. A runtime quality metric associated with the incoming tuples for the sliding window is measured, and the adaptation buffer is modified based on a difference between the quality threshold metric and the measured runtime quality metric.

Abstract: The present disclosure involves systems, software, and computer implemented methods for optimizing continuous queries for hybrid execution over a stream processing engine and an in-memory database. In one example, a method may include identifying a continuous query executed at a stream processing engine, the continuous query including a plurality of operators. An optimal plan for execution of the identified continuous query at the stream processing engine is determined. For each of the plurality of operators in the determined optimal plan, an optimized placement decision for executing a particular operator in the stream processing engine or at a database system is determined. An optimized continuous query is generated from the identified continuous query based on the determined optimal placement decisions for each of the plurality of operators in the determined optimal plan. The optimized continuous query is then executed at the stream processing engine and the database system.

Abstract: Allocation within a database environment is performed by processing a sequence of steps, each of which is described by an allocation processing block. The allocation processing block receives three inputs (input tables or input view) and computes a result. Related apparatus, systems, techniques and articles are also described.

Abstract: Allocation within a database environment is performed by processing a sequence of steps, each of which is described by an allocation processing block. The allocation processing block receives three inputs (input tables or input view) and computes a result. Related apparatus, systems, techniques and articles are also described.

Abstract: A reallocation processing block including a computing system including one or more data processors receives a base table, a reference table, and at least one assignment path table. Subsequently, rules from the at least one assignment path table are applied to the base table and the reference table by reallocating values between at least two existing data objects. A results table is generated with the reallocated values in the at least two existing data objects. A reallocated value is compared with a threshold value to determine the need for an iteration. At least one of the activities described is implemented using at least one data processor. Related apparatus, systems, techniques and articles are also described.

Abstract: A reallocation processing block including a computing system including one or more data processors receives a base table, a reference table, and at least one assignment path table. Subsequently, rules from the at least one assignment path table are applied to the base table and the reference table by reallocating values between at least two existing data objects. A results table is generated with the reallocated values in the at least two existing data objects. A reallocated value is compared with a threshold value to determine the need for an iteration. At least one of the activities described is implemented using at least one data processor. Related apparatus, systems, techniques and articles are also described.

Abstract: Allocation within a database environment is performed by processing a sequence of steps, each of which is described by an allocation processing block. The allocation processing block receives three inputs (input tables or input view) and computes a result. Related apparatus, systems, techniques and articles are also described.

Abstract: An enhanced data conversion framework, in which a data record in each of first and second data sources is populated with manually selected, representative sample data, the first and second data sources using different data storage schemas to store the representative sample data as instance values of instance elements. Parameters for a CONCATENATE function or an EXTRACT function are automatically determined based on a selected succession graph, and non-sample data is converted between the different data storage schemas of the first and second data sources, using the CONCATENATE function or the EXTRACT function.

Abstract: Described herein are systems and methods for importing and retrieving schema mappings while preserving privacy and confidentiality so that existing mappings can be reused across different customers without allowing reverse engineering of the original schemas. The disclosed embodiments provide different levels of mapping anonymity and correspondingly, available structural information in the retrieved mappings, in accordance with the security and privacy requirements.

Abstract: An enhanced data conversion framework, in which a data record in each of first and second data sources is populated with manually selected, representative sample data, the first and second data sources using different data storage schemas to store the representative sample data as instance values of instance elements. Parameters for a CONCATENATE function or an EXTRACT function are automatically determined based on a selected succession graph, and non-sample data is converted between the different data storage schemas of the first and second data sources, using the CONCATENATE function or the EXTRACT function.

Abstract: A method and system are described for managing data quality. An example method may include obtaining a first data stream interval including a first group of data items and a first aggregated data quality value associated with a quality of obtaining the first group, each data item including data attribute values, each data quality item including data quality attribute values associated with one of the data items. The first aggregated data quality value, a first indicator associating the first aggregated data quality value with the first group, and the first group may be selected. The first group and the first indicator may be stored in a user table of a database. A data quality table associated with the user table may be determined based on an entry in a system table. The first aggregated data quality value and the first indicator may be stored in the data quality table.

Abstract: A method and system are described for including data quality in data streams. An example method may include obtaining a first group of data items, each data item including one or more data attribute values. A first group of data quality items may be determined, each data quality item including one or more data quality attribute values associated with one of the data items of the first group. A first aggregated data quality value may be determined based on the first group of data quality items. A first data stream interval including the first group of data items and the first aggregated data quality value may be output.

Abstract: A remote device having the ability to transmit data across a plurality of wireless channels selects the transmission channel for transmitting the data message. The remote device prioritizes the data message based on an examination of the data message relative to predetermined priority settings. Based on this prioritization and in relation to a transmission prediction term, the remote device assigns the data message to a message queue associated with the selected transmission channel. When the remote device enters the transmission range of the selected channel, the data message may thereupon be wirelessly transmitted.

Abstract: Prioritized transmission of data from a mobile device, where the mobile device includes the ability to transmit data using more than one transmission medium, includes generating data packets from sensor data. The data packet is then assigned a priority level based on the sensor data and various factors relating to the transmission mediums. The priority level may be assigned based on the transmission availability, bandwidth and cost of using the medium. The data packets, based on the assigned priority level, are then stored in a corresponding priority buffer associated with a transmission device using one of the transmission mediums. When available, the transmission device may then readily transmit the data packet from the priority buffer. Therefore, the data is transferred in a prioritized order based, in part, on the transmission medium and factors relating to the medium, as well as the sensor data being transmitted.

Abstract: Deployment plans, to service execution environments, of component services associated with a composite service associated with an analysis of data generated by one or more data sources, may be determined, the composite service including an ordering of execution of the associated component services for the analysis of the data. An evaluation of each of the deployment plans of the component services may be determined based on a first metric associating one or more weighted values with a consumption by the each deployment plan of one or more respective resources associated with each of the first and second network nodes and on a second metric associating one or more weighted values with a measure of connection availability of one or more network links included in a communication path between the first and second network nodes. A recommendation including one or more of the deployment plans may be determined based on the evaluation.

Abstract: A method and system are described for including data quality in data streams. An example method may include obtaining a first group of data items, each data item including one or more data attribute values. A first group of data quality items may be determined, each data quality item including one or more data quality attribute values associated with one of the data items of the first group. A first aggregated data quality value may be determined based on the first group of data quality items. A first data stream interval including the first group of data items and the first aggregated data quality value may be output.