Abstract: Machine semantic perception is discussed. One example system can comprise an environmental knowledgebase (KB) associating features with properties, and an interface component receiving sensor data associated with observed properties. The KB and sensor observations can be encoded in bit-matrix and bit-vector representations, respectively, for efficient storage and computation. A perception component can perform semantic perception on observed properties based on the KB. The perception component can determine explanatory features associated with the observed properties through abductive reasoning, and determine discriminatory properties associated with the explanatory features through deductive reasoning. These can be executed in an iterative and interleaved Perception Cycle for efficient computation of minimum actionable explanations of observations.

Abstract: One or more embodiments of techniques or systems for sentiment extraction are provided herein. From a corpus or group of social media data which includes one or more expressions pertaining to a topic, target topic, or a target, one or more candidate expressions may be extracted. Relationships between one or more pairs of candidate expressions may be identified or evaluated. For example, a consistency relationship or an inconsistency relationship between a pair may be determined. A root word database may include one or more root words which facilitate identification of candidate expressions. Among one or more of the root words may be seed words, which may be associated with a predetermined polarity. To this end, polarities may be determined based on a formulation which assigns polarities to a sentiment expression, candidate expressions, or an expression as a constrained optimization problem.

Abstract: Our invention guarantees global serializability by preventing multidatabase transactions from being serialized in different ways at the participating local database systems (LDBS). In one embodiment tickets are used to inform the MDBS of the relative serialization order of the subtransactions of each global transactions at each LDBS. A ticket is a (logical) timestamp whose value is stored as a regular data item in each LDBS. Each substransaction of a global transaction is required to issue the take-a-ticket operations which consists of reading the value of the ticket (i.e., read ticket) and incrementing it (i.e., write (ticket+1)) through regular data manipulation operations. Only the subtransactions of global transactions take tickets. When different global transactions issue subtransactions at a local database, each subtransaction will include the take-a-ticket operations. Therefore, the ticket values associated with each global subtransaction at the MDBS reflect the local serialization order at each LDBS.