Abstract: A non-textual data searching system according to the invention is capable of searching non-textual data at semantic levels above the fundamental symbolic level. The general approach begins by indexing the non-textual data corpus in such a way as to facilitate searching. The indexing process results in a number of “keytroids” that represent clusters of fuzzy attribute vectors, where each fuzzy attribute vector represents a data event associated with one or more non-textual data points. The actual searching process is analogous to a conventional text-based search engine: a query vector, which identifies a number of fuzzy attributes of the desired data, is processed to retrieve and rank a number of keytroids. The keytroids can be inverse-mapped to obtain data events and/or non-textual data points that satisfy the query.

Abstract: A non-textual data searching system according to the invention is capable of searching non-textual data at semantic levels above the fundamental symbolic level. The general approach begins by indexing the non-textual data corpus in such a way as to facilitate searching. The indexing process results in a number of “keytroids” that represent clusters of fuzzy attribute vectors, where each fuzzy attribute vector represents a data event associated with one or more non-textual data points. The actual searching process is analogous to a conventional text-based search engine: a query vector, which identifies a number of fuzzy attributes of the desired data, is processed to retrieve and rank a number of keytroids. The keytroids can be inverse-mapped to obtain data events and/or non-textual data points that satisfy the query.

Abstract: A non-textual data searching system according to the invention is capable of searching non-textual data at semantic levels above the fundamental symbolic level. The general approach begins by indexing the non-textual data corpus in such a way as to facilitate searching. The indexing process results in a number of “keytroids” that represent clusters of fuzzy attribute vectors, where each fuzzy attribute vector represents a data event associated with one or more non-textual data points. The actual searching process is analogous to a conventional text-based search engine: a query vector, which identifies a number of fuzzy attributes of the desired data, is processed to retrieve and rank a number of keytroids. The keytroids can be inverse-mapped to obtain data events and/or non-textual data points that satisfy the query.

Abstract: A crossing network that matches buy and sell orders based upon a satisfaction and quantity profile is disclosed. The crossing network includes a number of trader terminals that can be used for entering orders. The orders are entered in the form of a satisfaction density profile that represents a degree of satisfaction to trade a particular instrument at various (price, quantity) combinations. Typically, each order is either a buy order or a sell order. The trader terminals are coupled to a matching controller computer. The matching controller computer can receive as input the satisfaction density profiles entered at each one of the trading terminals. The matching controller computer matches orders (as represented by each trader's satisfaction density profile) so that each trader is assured that the overall outcome of the process (in terms of average price and size of fill) has maximized the mutual satisfaction of all traders. Typically, the matching process is anonymous.

Abstract: A crossing network that matches buy and sell orders based upon a satisfaction and quantity profile is disclosed. The crossing network includes a number of trader terminals that can be used for entering orders. The orders are entered in the form of a satisfaction density profile that represents a degree of satisfaction to trade a particular instrument at various (price, quantity) combinations. Typically, each order is either a buy order or a sell order. The trader terminals are coupled to a matching controller computer. The matching controller computer can receive as input the satisfaction density profiles entered at each one of the trading terminals. The matching controller computer matches orders (as represented by each trader's satisfaction density profile) so that each trader is assured that the overall outcome of the process (in terms of average price and size of fill) has maximized the mutual satisfaction of all traders. Typically, the matching process is anonymous.