IBM Patent Applications

CLUSTER FAMILIES FOR CLUSTER SELECTION AND COOPERATIVE REPLICATION

Granted: August 17, 2017
Application Number: 20170235508
Cluster families for cluster selection and cooperative replication are created. The clusters are grouped into family members of a cluster family base on their relationships and roles. Members of the cluster family determine which family member is in the best position to obtain replicated information and become cumulatively consistent within their cluster family. Once the cluster family becomes cumulatively consistent, the data is shared within the cluster family so that all copies within…

DISTRIBUTED LOAD PROCESSING USING FORECASTED LOCATION-BASED INTERNET OF THINGS DEVICE CLUSTERS

Granted: August 17, 2017
Application Number: 20170235603
For distributed processing using forecasted location-based IoT device clusters, at a central IoT device, a data source that is to be used and a duration for processing a workload is determined. A set of IoT devices operating within a threshold distance from the data source at a first time is selected. A first subset of the IoT devices is selected to form a cluster of IoT devices where each IoT device satisfies a clustering condition. A second subset of the first subset is selected to…

DISTRIBUTED LOAD PROCESSING USING CLUSTERS OF INTERDEPENDENT INTERNET OF THINGS DEVICES

Granted: August 17, 2017
Application Number: 20170235604
For distributed processing using clustering of interdependent Internet of Things (IoT) devices, at a central device, a data source to be used for processing a workload is determined. A set is selected of devices operating within a threshold distance from the data source at a first time. A first subset of the set of devices is selected. Each device in the first subset satisfies a clustering condition. A first device in the subset is instructed to configure a lightweight application to…

DISTRIBUTED LOAD PROCESSING USING SAMPLED CLUSTERS OF LOCATION-BASED INTERNET OF THINGS DEVICES

Granted: August 17, 2017
Application Number: 20170235616
For distributed processing using sampled clusters of location-based Internet of Things (IoT) devices, at a central device, a data source to be used for processing a workload is determined. A set is selected of devices operating within a threshold distance from the data source at a first time. A first subset including a first sample number of devices is selected from the set. A ratio is determined of a first amount of a computing resource needed to process the workload and a second amount…

DISTRIBUTED LOAD PROCESSING USING DRIFT-BASED DYNAMIC CLUSTERS OF INTERNET OF THINGS DEVICES

Granted: August 17, 2017
Application Number: 20170237804
For distributed processing using drift-based dynamic clustering of Internet of Things (IoT) devices, at a central device, a data source to be used for processing a workload is determined. A set is selected of devices operating within a threshold distance from the data source at a first time. A first subset of the set of devices is selected to form a cluster of devices. Each device in the first subset satisfies a clustering condition. A first device in the first subset is instructed to…

ESTABLISHING A LOGICAL CONFIGURATION FOR A DATA STORAGE LIBRARY

Granted: August 10, 2017
Application Number: 20170228165
A system to configure a storage library, by a processor. A first storage library comprising a plurality of host adapters, a data cache, and a plurality of device adapters is supplied. The first storage library is in communication via fibre channel with a storage area network comprising a storage virtual controller, and the storage area network comprises a configuration library. The storage area network is in communication with a host computer comprising a command line interface and an…

DYNAMIC RECOMPILATION TECHNIQUES FOR MACHINE LEARNING PROGRAMS

Granted: August 10, 2017
Application Number: 20170228222
The embodiments described herein relate to recompiling an execution plan of a machine-learning program during runtime. An execution plan of a machine-learning program is compiled. In response to identifying a directed acyclic graph of high-level operations (HOP DAG) for recompilation during runtime, the execution plan is dynamically recompiled. The dynamic recompilation includes updating statistics and dynamically rewriting one or more operators of the identified HOP DAG, recomputing…

COPY-ON-WRITE IN CACHE FOR ENSURING DATA INTEGRITY IN CASE OF STORAGE SYSTEM FAILURE

Granted: August 10, 2017
Application Number: 20170228314
Various embodiments for managing data integrity in a computing storage environment, by a processor device, are provided. In one embodiment, a method comprises applying a copy-on-write technique to a cache in a computer storage system such that each write arriving in the cache is assigned to a separate new physical location and registered sequentially in an order the write arrived, for preserving a state of the computer storage system during a failure event.

THRESHOLDING TASK CONTROL BLOCKS FOR STAGING AND DESTAGING

Granted: August 10, 2017
Application Number: 20170228324
For thresholding task control blocks (TCBs) for staging and destaging, a first tier of TCBs are reserved for guaranteeing a minimum number of TCBs for staging and destaging for storage ranks. An additional number of requested TCBs are apportioned from a second tier of TCBs to each of the storage ranks based on a scaling factor that is calculated at predefined time intervals. The scaling factor is multiplied by a total number of a plurality of requests from each of the storage ranks for…

RECOGNITION-BASED COLOR CORRECTIONS

Granted: August 10, 2017
Application Number: 20170228611
A method comprising: performing a first object recognition round on an image to detect at least a first object; matching the first detected object to a first reference object, thereby recognizing the first object; determining a chromatic adaptation transform between the first recognized object and the first reference object; applying the chromatic adaptation transform to the image; performing a second object recognition round on the chromatically adapted image to detect a second object…

INTEGRATED CIRCUIT (IC) WITH OFFSET GATE SIDEWALL CONTACTS AND METHOD OF MANUFACTURE

Granted: August 10, 2017
Application Number: 20170229479
A method of forming logic cell contacts, forming CMOS integrated circuit (IC) chips including the FETs and the IC chips. After forming replacement metal gates (RMG) on fin field effect transistor (finFET) pairs, gates are cut on selected pairs, separating PFET gates from NFET gates. An insulating plug formed between the cut gates isolates the pairs of cut gates from each other. Etching offset gate contacts at the plugs partially exposes each plug and one end of a gate sidewall at each…

FORECASTING AND CLASSIFYING CYBER-ATTACKS USING NEURAL EMBEDDINGS

Granted: August 10, 2017
Application Number: 20170230398
A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one the vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection, or both. Using a…

FORECASTING AND CLASSIFYING CYBER ATTACKS USING NEURAL EMBEDDINGS MIGRATION

Granted: August 10, 2017
Application Number: 20170230399
A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by migrating, at least one of a vectors of the second collection with a corresponding vector of a fourth collection. The second and the fourth collections have a property distinct from one another. Using a forecasting configuration, a vector of the…

FORECASTING AND CLASSIFYING CYBER-ATTACKS USING ANALYTICAL DATA BASED NEURAL EMBEDDINGS

Granted: August 10, 2017
Application Number: 20170230400
A first collection including an analytical feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection. Using a…

FORECASTING AND CLASSIFYING CYBER-ATTACKS USING NEURAL EMBEDDINGS BASED ON PATTERN OF LIFE DATA

Granted: August 10, 2017
Application Number: 20170230401
A first collection including a pattern of life (POL) feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection.…

FORECASTING AND CLASSIFYING CYBER-ATTACKS USING CROSSOVER NEURAL EMBEDDINGS

Granted: August 10, 2017
Application Number: 20170230407
A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection. The second and the fourth collections have a property similar to one another. Using a forecasting configuration, a vector of the…

DETECTING AND PREDICTING CYBER-ATTACK PHASES IN DATA PROCESSING ENVIRONMENT REGIONS

Granted: August 10, 2017
Application Number: 20170230408
A set of collections of forecasted feature vectors is selected from a repository for a future time window after a present time, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set having feature vectors that are indicative of an event related to the cyber-attack in a region of the environment at a discrete time. The events corresponding to the collections in the set are classified into a class of cyber-attack. From a mapping…

DETECTING AND PREDICTING CYBER-ATTACK PHASES IN ADJACENT DATA PROCESSING ENVIRONMENT REGIONS

Granted: August 10, 2017
Application Number: 20170230409
A set and a second set of collections of forecasted feature vectors are selected from a repository for a future time window, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set and a collection in the second set indicating an event related to the cyber-attack in a first region and a second event in a second region, respectively, of the environment at a discrete time. The set of collections is input at a first input and the second…

FOCUS COORDINATION IN GEOGRAPHICALLY DISPERSED SYSTEMS

Granted: August 10, 2017
Application Number: 20170230431
A method, system, and computer program product for focus coordination in geographically dispersed systems are provided in the illustrative embodiments. A shifting of focus to a first object present in a first view is detected at a first data processing system in a first location in the geographically dispersed plurality of data processing systems. Metadata of the first view is identified, the metadata being usable to identify a second object in a second view at a second data processing…

LOCALIZING FAULTS IN WIRELESS COMMUNICATION NETWORKS

Granted: August 10, 2017
Application Number: 20170230851
Various embodiments manage service issues within a wireless communication network. In one embodiment, a one or more call detail records associated with a set of wireless communication devices of a wireless communication network is received. A set of information within each of the one or more call detail records is compared to a baseline statistical model. The baseline statistical model identifies a normal operating state of the wireless communication network. At least one outlier call…