Abstract: A method including receiving first data including a feature corresponding to an application, receiving second data including a specification of a component included in a device, analyzing a performance of the device based on the first data and the second data using a model, and modifying the specification based on the performance of the device.

Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved table identification in a spreadsheet. One method including: receiving a spreadsheet including at least one table; identifying, using machine learning, one or more classes of a plurality of classes for each cell of the received spreadsheet, wherein the plurality of classes include corners and not-a-corner; and inducing at least one table in the received spreadsheet based on the one or more identified classes for each cell of the received spreadsheet.

Abstract: System and methods for IoT event detector correctness verification. Detector models (e.g., state-based models including variables, states, transitions and actions) take IoT device data as input and detect, based on the data, events that triggers actions. To verify a correctness of the models prior to deploying the models at scale, an event detector model correctness checker obtains a representation of a definition of the model, verifies, based on analysis of the model definition, whether the model complies with correctness properties, and generates a report indicating whether the model complies. Example correctness properties include a reachability correctness property that indicates that respective states or actions are reachable according to the definition of the event detector model. The analysis may be accessed via an interface element and may result in generation of a report that identifies a location of non-compliance within the model definition.

Abstract: System and methods for IoT event detector correctness verification. Detector models (e.g., state-based models including variables, states, transitions and actions) take IoT device data as input and detect, based on the data, events that triggers actions. To verify a correctness of the models prior to deploying the models at scale, an event detector model correctness checker obtains a representation of a definition of the model, verifies, based on analysis of the model definition, whether the model complies with correctness properties, and generates a report indicating whether the model complies. Example correctness properties include a reachability correctness property that indicates that respective states or actions are reachable according to the definition of the event detector model. The analysis may be accessed via an interface element and may result in generation of a report that identifies a location of non-compliance within the model definition.

Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved table identification in a spreadsheet. One method including: receiving a spreadsheet including at least one table; identifying, using machine learning, one or more classes of a plurality of classes for each cell of the received spreadsheet, wherein the plurality of classes include corners and not-a-corner; and inducing at least one table in the received spreadsheet based on the one or more identified classes for each cell of the received spreadsheet.

Abstract: A computing device includes a storage machine holding instructions executable by a logic machine to generate multi-string clusters, each containing alphanumeric strings of a dataset. Further multi-string clusters are generated via iterative performance of a combination operation in which a hierarchically-superior cluster is generated from a set of multi-string clusters. The combination operation includes, for candidate pairs of multi-string clusters, generating syntactic profiles describing an alphanumeric string from each multi-string cluster of the candidate pair. For each of the candidate pairs, a cost factor is determined for at least one of its syntactic profiles. Based on the cost factors determined for the syntactic profiles, one of the candidate pairs is selected. The multi-string clusters from the selected candidate pair are combined to generate the hierarchically-superior cluster including all of the alphanumeric strings from the selected candidate pair of multi-string clusters.

Abstract: A method for generating a smaller dataset from a larger dataset, each dataset holding a plurality of records, includes profiling the larger dataset to identify a plurality of patterns, each of which is descriptive of one or more records held in the larger dataset. A plurality of slots of the smaller dataset is filled with records held in the larger dataset. Multiple records held in the larger dataset are individually retrieved, and for each retrieved record it is determined whether to place the retrieved record into a slot of the smaller dataset and evict a record already occupying that slot, or not place the retrieved record into the smaller dataset. This determination is based on a pattern of the retrieved record and a representation status of the pattern in the smaller dataset.

Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved table identification in a spreadsheet. One method including: receiving a spreadsheet including at least one table; identifying, using machine learning, one or more classes of a plurality of classes for each cell of the received spreadsheet, wherein the plurality of classes include corners and not-a-corner; and inducing at least one table in the received spreadsheet based on the one or more identified classes for each cell of the received spreadsheet.

Abstract: A computing device includes a storage machine holding instructions executable by a logic machine to generate multi-string clusters, each containing alphanumeric strings of a dataset. Further multi-string clusters are generated via iterative performance of a combination operation in which a hierarchically-superior cluster is generated from a set of multi-string clusters. The combination operation includes, for candidate pairs of multi-string clusters, generating syntactic profiles describing an alphanumeric string from each multi-string cluster of the candidate pair. For each of the candidate pairs, a cost factor is determined for at least one of its syntactic profiles. Based on the cost factors determined for the syntactic profiles, one of the candidate pairs is selected. The multi-string clusters from the selected candidate pair are combined to generate the hierarchically-superior cluster including all of the alphanumeric strings from the selected candidate pair of multi-string clusters.

Abstract: A computing device includes a storage machine holding instructions executable by a logic machine to generate multi-string clusters, each containing alphanumeric strings of a dataset. Further multi-string clusters are generated via iterative performance of a combination operation in which a hierarchically-superior cluster is generated from a set of multi-string clusters. The combination operation includes, for candidate pairs of multi-string clusters, generating syntactic profiles describing an alphanumeric string from each multi-string cluster of the candidate pair. For each of the candidate pairs, a cost factor is determined for at least one of its syntactic profiles. Based on the cost factors determined for the syntactic profiles, one of the candidate pairs is selected. The multi-string clusters from the selected candidate pair are combined to generate the hierarchically-superior cluster including all of the alphanumeric strings from the selected candidate pair of multi-string clusters.

Abstract: A computing device includes a storage machine holding instructions executable by a logic machine to generate multi-string clusters, each containing alphanumeric strings of a dataset. Further multi-string clusters are generated via iterative performance of a combination operation in which a hierarchically-superior cluster is generated from a set of multi-string clusters. The combination operation includes, for candidate pairs of multi-string clusters, generating syntactic profiles describing an alphanumeric string from each multi-string cluster of the candidate pair. For each of the candidate pairs, a cost factor is determined for at least one of its syntactic profiles. Based on the cost factors determined for the syntactic profiles, one of the candidate pairs is selected. The multi-string clusters from the selected candidate pair are combined to generate the hierarchically-superior cluster including all of the alphanumeric strings from the selected candidate pair of multi-string clusters.

Abstract: A method for generating a smaller dataset from a larger dataset, each dataset holding a plurality of records, includes profiling the larger dataset to identify a plurality of patterns, each of which is descriptive of one or more records held in the larger dataset. A plurality of slots of the smaller dataset is filled with records held in the larger dataset. Multiple records held in the larger dataset are individually retrieved, and for each retrieved record it is determined whether to place the retrieved record into a slot of the smaller dataset and evict a record already occupying that slot, or not place the retrieved record into the smaller dataset. This determination is based on a pattern of the retrieved record and a representation status of the pattern in the smaller dataset.