Abstract: A photon peak event detection system accepts an analog output from a photon sensor, directly digitizes the analogy output and includes a graphics processing unit (GPU) programmed to conduct a photon peak event detection in real-time via a photon count program that analyzes the digitized photon sensor output in sampling periods each having at least three consecutive data points to determine a local maximum among the consecutive data points and compare the local maximum to one or more predetermined thresholds to determine whether or not a photon was received in each sampling period, the algorithm providing photon counts to a phasor analysis program in the GPU. The phasor analysis program calculates pixelwise fluorescence lifetime phasor data in real-time and sends the data to a central processing unit.

Abstract: Provided are solid dispersions, solid molecular complexes, salts and crystalline polymorphs involving propane-1-sulfonic acid {3-[5-(4-chloro-phenyl)-1H-pyrrolo[2,3-b]pyridine-3-carbonyl]-2,4-difluoro-phenyl}-amide.

Abstract: Provided are solid dispersions, solid molecular complexes, salts and crystalline polymorphs involving propane-1-sulfonic acid {3-[5-(4-chloro-phenyl)-1H-pyrrolo[2,3-b]pyridine-3-carbonyl]-2,4-difluoro-phenyl}-amide.

Abstract: The application relates to solid pharmaceutical compositions suitable for oral administration, processes for their production and uses of the pharmaceutical compositions.

Abstract: Provided are solid dispersions, solid molecular complexes, salts and crystalline polymorphs involving propane-1-sulfonic acid {3-[5-(4-chloro-phenyl)-1H-pyrrolo[2,3-b]pyridine-3-carbonyl]-2,4-difluoro-phenyl}-amide.

Abstract: Provided are solid dispersions, solid molecular complexes, salts and crystalline polymorphs involving propane-1-sulfonic acid {3-[5-(4-chloro-phenyl)-1H-pyrrolo[2,3-b]pyridine-3-carbonyl]-2,4-difluoro-phenyl}-amide.

Abstract: Provided are solid dispersions, solid molecular complexes, salts and crystalline polymorphs involving propane-1-sulfonic acid {3-[5-(4-chloro-phenyl)-1H-pyrrolo[2,3-b]pyridine-3-carbonyl]-2,4-difluoro-phenyl}-amide.

Abstract: Provided are solid dispersions, solid molecular complexes, salts and crystalline polymorphs involving propane-1-sulfonic acid {3-[5-(4-chloro-phenyl)-1H-pyrrolo[2,3-b]pyridine-3-carbonyl]-2,4-difluoro-phenyl}-amide.

Abstract: A method is described herein for causing an analytical database server to store query results in a memory. In the method, a first header element and a request to query an analytical database is received. The first header element indicates that a query result should be stored. The analytical database is queried according to the request, and the query result is stored. An identifier associated with the query result and a second header element corresponding to the identifier is then transmitted.

Abstract: A realtime training model update architecture for data mining models. The architecture facilitates automatic update processes with respect to evolving source/training data. Additionally, model update training can be performed at times other than in realtime. Scheduling can be invoked, for periodic and incremental updates, and refresh intervals applied through the training parameters for the mining structure and/or model. Training can also be triggered by user-defined events such as database notifications, and/or alerts from other operational systems. In support thereof, a data mining model component is provided for training a data mining model on a dataset in realtime, and an update component for incrementally training the data mining model according to predetermined criteria. Additionally, model versioning and version comparison can be employed to detect significant changes and retain updated models. Training data aging/weighting of training data can be applied.

Abstract: A standard mechanism for directly accessing unstructured data types (e.g., image, audio, video, gene sequencing and text data) in accordance with data mining operations is provided. The subject innovation can enable access to unstructured data directly from within the data mining engine or tool. Accordingly, the innovation enables multiple vendors to provide algorithms for mining unstructured data on a data mining platform (e.g., an SQL-brand server), thereby increasing adoption. As well, the subject innovation allows users to directly mine unstructured data that is not fixed-length, without pre-processing and tokenizing the data external to the data mining engine. In accordance therewith, the innovation can provide a mechanism to expand declarative language content types to include an “unstructured” data type thereby enabling a user and/or application to affirmatively designate mining data as an unstructured type.

Abstract: A system that effectuates fetching a complete set of relational data into a mining services server and subsequently defining desired partitions upon the fetched data is provided. In accordance with the innovation, the data can be locally cached and partitioned therefrom. Accordingly, upon the same mining structure (e.g., cache) that has been partitioned, the novel innovation can build mining models for each partition. In other words, the innovation can employ the concept of mining structure as a data cache while manipulating only partitions of this cache in certain operations. The innovation can be employed in scenarios where a user wants to train a mining model using only data points that satisfy a particular Boolean condition, a user wants to split the training set into multiple partitions (e.g., training/testing) and/or a user wants to perform a data mining procedure known as “N-fold cross validation.

Abstract: A drill-through feature is provided which provides a universal drill-through to mining model source data from a trained mining model. In order for a user or application to obtain model content information on a given node of a model, a universal function is provided whereby the user specifies the node for a model and data set, and the cases underlying that node for that model and data set are returned. A sampling of underlying cases may be provided, where only a sampling of the cases represented in the node is requested.

Abstract: A method is described herein for causing an analytical database server to store query results in a memory. In the method, a first header element and a request to query an analytical database is received. The first header element indicates that a query result should be stored. The analytical database is queried according to the request, and the query result is stored. An identifier associated with the query result and a second header element corresponding to the identifier is then transmitted.

Abstract: The subject invention relates to systems and methods that extend the network data access capabilities of mark-up language protocols. In one aspect, a network data transfer system is provided. The system includes a protocol component that employs a computerized mark-up language to facilitate data interactions between network components, whereby the data interactions were previously limited or based on a statement command associated with the markup language. An extension component operates with the protocol component to support the data transactions, where the extension component supplies at least one other command from the statement command to facilitate the data interactions.

Abstract: The subject disclosure pertains to extensible data mining systems, means, and methodologies. For example, a data mining system is disclosed that supports plug-in or integration of non-native mining algorithms, perhaps provided by third parties, such that they function the same as built-in algorithms. Furthermore, non-native data mining viewers may also be seamlessly integrated into the system for displaying the results of one or more algorithms including those provided by third parties as well as those built-in. Still further yet, support is provided for extending data mining languages to include user-defined functions (UDFs).

Abstract: A system that facilitates data mining comprises a reception component that receives command(s) in a declarative language that relate to utilizing an output of a first data mining model as an input to a second data mining model. An implementation component analyzes the received command(s) and implements the command(s) with respect to the first and second data mining models. In another aspect of the subject invention, the reception component can receive further command(s) in a declarative language with respect to causing one or more of the first and second data mining models to output a prediction, the prediction desirably generated without prediction input, the implementation component causes the one or more of the first and second data mining models to output the prediction.

Abstract: Architecture that facilitates syntax processing for data mining statements. The system includes a syntax engine that receives as an input a query statement which, for example, is a data mining request. The statement can be generated from many different sources, e.g., a client application and/or a server application, and requests query processing of a data source (e.g., a relational database) to return a result set. The syntax engine includes a binding component that converts the query statement into an encapsulated statement in accordance with a predefined grammar. The encapsulated statement includes both data and data operations to be performed on the data of the data source, and which is understood by the data source. An execution component processes the encapsulated statement against the data source to return the desired result set.

Abstract: In order to allow the use of data in dimension attributes for grouping members of a dimension, dimension attribute data is analyzed so it can be used as if it were data for a categorical attribute with a manageable number of states. The values possible for the dimension attribute are divided into groups. This is done by determining the distribution of data. An approximate distribution may be determined (by sampling some data) or an actual distribution may be determined (by sampling all data). The distribution is then used to determine the groups into which the range of data values will be divided. Each group is then treated as if it were a state for a categorical-type dimension attribute. A state can be determined for a member by determining which subrange contains the value for the dimension attribute for the member. The number of groups can be determined by a user or determined dynamically, e.g. to best fit the distribution found.

Abstract: A mining structure is created which contains processed data from a data set. This data may be used to train one or more models. In addition to the selection of data to be used by model from data set, processing parameters are set, in one embodiment. For example, the discretization of a continuous variable into buckets, the number of buckets, and/or the sub-range corresponding to each bucket is set when the mining structure is created. The mining structure is processed, which causes the processing and storage of data from data set in the mining structure. After processing, the mining structure can be used by one or more models.