Decisions with Big Data

A method for predictive analytics, one that utilizes vector entities archived from past design or planning projects, for the purpose of providing predictions aiding new design or planning projects, a method that further utilizes

• a database access step, for accessing the vector entities, with one vector entity representing an input vector from a past design or planning project, and another vector representing a corresponding known good output vector,
• with a known good output vector being defined as an output vector that fulfills original requirements imposed on the archived design or planning project,
• a generic system model access step, for relating the output vector to the input vector, where the system model is derived from the archived vector entities, and where
• the system model can be made capable of supporting input vectors with elements taking on continuous or categorical values, where the values are confined to a continuous range or not,
• the system model can be derived using multivariate linear regression, especially in case of input data set of small to medium size, or can be implemented using neural networks, especially in case of input data set of large size,
• the system model can be made capable of approximating a generic continuous-valued system model with a two-layer feed-forward neural network,
• a prediction step, for producing a guiding reference for a new design or planning project, a reference obtained as output from the system model when applying a vector from the new design or planning project as input,
• an optional access step to an external database or databases, for querying for pertinent reference information helpful to the context of the new design or planning project, when necessary.

A method for querying a database, for the purpose of efficiently identifying archived database items matching a new user input query, a method utilizing

• a representation step, involving a sequence of text processes, for purpose of identifying vocabulary of terms in the user input query or in corpus of database items,
• a weighting step, in which a weighting scheme is applied to the vocabulary of terms, with resultant weights becoming values in a matrix for latent semantic analysis,
• a comparison step, based on latent semantic analysis, in which the degree of relevance between the input query and the corpus of database items is predicted, for the purpose of efficiently matching the input query with the corpus items,
• a retrieval step, for retrieving the database corpus items most relevant to the user input query.