METHODS, APPARATUS AND SYSTEMS FOR DATA VISUALIZATION AND RELATED APPLICATIONS

• US 20110179066A1
• Filed: 06/18/2009
• Published: 07/21/2011
• Est. Priority Date: 06/20/2008
• Status: Abandoned Application

• Current Assignee: New BIS Safe Luxco S.A R.L
• Original Assignee: Business Intelligence Solutions Safe B.V.
• Inventors: Ingham, Peter Stewart, Cardno, Andrew John, Lewin, Bart Andrew, Singh, Ashok Kumar
• Application Number:
  US13/000,267
• Publication Number:
  US 20110179066A1
• Time in Patent Office: Days
• US Class Current: 707/769
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor

Abstract

In a graphical analysis computing system, a method of arranging data sets for graphical analysis, wherein at least two of the data sets have different periodicities, the method comprising the steps of: a data retrieval module retrieving data from a data storage module in communication with the graphical analysis computing system; a periodicity determination module determining a plurality of periodicities within the retrieved data to identify a plurality of data sets based on the determined periodicities; and an alignment module aligning a first identified data set of a first periodicity relative to a second identified data set of a second periodicity, wherein the second periodicity is different to the first periodicity.

Claims

1-93. -93. (canceled)

94. In a graphical analysis computing system, a method of arranging data sets for graphical analysis, wherein at least two of the data sets have different periodicities, the method comprising the steps of:

a. a data retrieval module retrieving data from a data storage module in communication with the graphical analysis computing system;

b. a periodicity determination module determining a plurality of periodicities within the retrieved data to identify a plurality of data sets based on the determined periodicities; and

c. an alignment module distorting a first identified data set of a first periodicity so that it maps onto a single undistorted identified data set of a second periodicity, wherein the second periodicity is different to the first periodicity.

95. The method of claim 94 further including the step of distorting a plurality of first identified data sets relative to the second identified data set.

97. The method of claim 94, wherein step c) further includes the step of aligning a start period of the first identified data set with the start period of the second identified data set.

96. The method of claim 94, wherein step c) further includes the step of aligning an end period of the first identified data set with an end period of the second identified data set.

98. The method of claim 94, wherein step c) further includes the step of determining whether the first identified data set is wholly encompassed within the second identified data set.

99. The method of claim 98, whereupon a negative determination, the method further includes the step of distorting the first identified data set so that it is wholly encompassed within the second identified data set.

100. The method of claim 94, wherein step c) further includes the step of modifying the first identified data set so that it aligns with the second identified data set.

101. The method of claim 100, further including the step of restricting the first identified data set so that it aligns with the second identified data set.

102. The method of claim 100, further including the step of expanding the first identified data set so that it aligns with the second identified data set.

103. The method of claim 100, further including the step of analyzing the modified first identified data set.

104. The method of claim 100, further including the step of replacing the first identified data set with the modified first identified data set.

105. The method of claim 94 wherein the periodicity is time related.

106. The method of claim 94 wherein the periodicity is calendar related.

107. The method of claim 94, wherein a lowest common time base is determined from the determined periodicities, and the first identified data set is based on the determined lowest common time base.

108. The method of claim 94, wherein the distortion of the first identified data set includes the step of arranging the graphical representation of the first identified data set so that it aligns with a spatial area allocated for the graphical representation of the second identified data set.

109. The method of claim 108, further including the step of condensing the graphical representation of the first identified data set so that it aligns with the spatial area.

110. The method of claim 108, further including the step of expanding the graphical representation of the first identified data set so that it aligns with the spatial area.

111. The method of claim 108, further including the step of moving the graphical representation of the first identified data set so that it aligns with the spatial area.

112. The method of claim 108, wherein the spatial area is at least a portion of a dodecagon spiral.

113. The method of claim 108, wherein the spatial area is at least a portion of a box spiral.

114. The method of claim 108, wherein the first and second identified data sets are displayed hierarchically.

information associated with how the first and second data sets are aligned.

115. The method of claim 94, wherein a graphical representation of the first identified data set is distorted to align it to the determined periodicity of the second identified data set.

116. The method of claim 115, wherein the degree of distortion is increased as the graphical representation of the first identified data set is viewed at increased granularity.

117. A method as claimed in claim 94, wherein a graphical representation of the first identified data set can be viewed at increasing granularity, and the method includes the further steps of:

identifying a further data set at the increased granularity, wherein the further data set is based on a further periodicity different to the first and second periodicities; and

distorting the further identified data set relative to the second identified data set.

118. In a graphical analysis computing system, a method of arranging, for graphical analysis, periodic data sets including periodic events, the method comprising the steps of:

a. a data retrieval module retrieving data from a data storage module in communication with the graphical analysis computing system;

b. a periodicity determination module determining periodicities within the retrieved data;

c. identifying a plurality of data sets based on the determined periodicities; and

d. identifying an instance of a periodic event within two or more identified data sets; and

e. an alignment module distorting one, or both, of data and its graphical representation associated with at least one of the identified data sets to map a first identified data set of a first periodicity onto a single undistorted identified data set of a second periodicity and align the identified periodic event instances relative to each other.

119. The method of claim 118, wherein one or both of the data and its graphical representation is distorted in only one of the identified data sets

120. The method of claim 118, wherein the determined periodicity is a period of time selected from a multiple, whole or portion of a second, minute, hour, day, week, month, or year.

121. The method of claim 118, wherein the determined periodicity is a multiple, whole or portion of a calendar period.

122. The method of claim 118, wherein the determined periodicity is a multiple, whole or portion of a social or business period.

123. The method of claim 118 further including the steps of:

the periodicity determination module identifying a first instance of the periodic event within a first data set within a first group of data sets having a first periodicity,

identifying a second instance of the periodic event within a second data set within a second group of data sets having a first periodicity, and

determining whether the first data set is in a same or different relative position within the first group to the position of the second data set in the second group, and

the alignment module aligning one or both of the data and its graphical representation associated with the first and second instances of the periodic event according to the position determination.

124. The method of claim 123, whereupon the determination that the first data set is in a different relative position, the method further includes the steps of:

the alignment module aligning the first data set in the first group with the second data set within the second group, and

aligning the data associated with the first instance of the periodic event in the first data set with the data associated with the second instance of the periodic event in the second data set.

125. The method of claim 123, whereupon the determination that the first data set is in the same relative position, the method further includes the step of:

the alignment module aligning the data associated with the first instance of the periodic event in the first data set with the data associated with the second instance of the periodic event in the second data set.

126. The method of claim 123, further including the step of the alignment module modifying the data within the first data set associated with the first instance to align the first and second instances of the periodic event.

127. The method of claim 126, further including the step of the alignment module restricting at least a portion of the data within the first data set so that the first instance of the periodic event aligns with the second instance of the periodic event.

128. The method of claim 126, further including the step of the alignment module expanding at least a portion of the data within the first data set so that the first instance of the periodic event aligns with the second instance of the periodic event.

129. The method of claim 126, further including the step of replacing the first data set with the modified first data set.

130. The method of claim 118, wherein the alignment of the first data set includes the step of distorting the graphical representation of the first instance of the periodic event so that it aligns with the graphical representation of the second instance of the periodic event.

131. The method of claim 130, further including the step of condensing the graphical representation of the first data set so that the first instance of the periodic event aligns with the second instance of the periodic event.

data set to provide a condensed graphical representation of the first data set.

132. The method of claim 130, further including the step of expanding the graphical representation of the first data set so that the first instance of the periodic event aligns with the second instance of the periodic event.

133. The method of claim 130, further including the step of moving at least a portion of the graphical representation of the first data set so that the first instance of the periodic event aligns with the second instance of the periodic event.

134. The method of claim 130, wherein the graphical representation is a dodecagon spiral.

135. The method of claim 130, wherein the graphical representation is a box spiral.

136. The method of claim 130, wherein the data sets are displayed in the form representing two or more calendar systems.

137. The method of claim 130, wherein the first and second data sets are displayed hierarchically.

138. The method of claim 130 herein the degree of distortion is increased as the graphical representation of the first data set is viewed at increased granularity.

139. In a temporal query system, a method of constructing queries against a plurality of data sets having different periodicities comprising:

a. a determination module determining the periodicity of the plurality of data sets;

b. a query resolving module resolving the temporal parameters passed in the query; and

c. a data set creation module creating data sets according to the resolved parameters by mapping a first identified data set of a first periodicity onto a single undistorted identified data set of a second periodicity.

140. A method as claimed in claim 139 where the input parameters are times in different time zones.

141. A method as claimed in claim 139 where the calculations of temporal or relationships functions are built on an extension of SQL.

142. A method as claimed in claim 139 where the calculations or temporal functions or relationships use metadata to provide sensible defaults for the interpretation of results.

143. A method as claimed in claim 139 where a rules engine in communication with the query resolving module is used to resolve queries giving an answer that is most likely to be correct based on a set of rules applied to the engine.

144. A method as claimed in claim 139 where the implementation of the query results is produced as a result of an extended SQL query against an extended relational database.

145. A graphical analysis computing system for arranging data sets for graphical analysis, wherein at least two of the data sets have different periodicities, the system comprising

a data retrieval module arranged to retrieve data from a data storage module in communication with the graphical analysis computing system;

a periodicity determination module arranged to determine a plurality of periodicities within the retrieved data to identify a plurality of data sets based on the determined periodicities; and

an alignment module arranged to distort a first identified data set of a first periodicity so that it maps onto a single undistorted identified data set of a second periodicity, wherein the second periodicity is different to the first periodicity.

146. A graphical analysis computing system for arranging, for graphical analysis, periodic data sets including periodic events, the system comprising

a data retrieval module arranged to retrieve data from a data storage module in communication with the graphical analysis computing system;

a periodicity determination module arranged to determine periodicities within the retrieved data;

identify a plurality of data sets based on the determined periodicities; and

identify an instance of a periodic event within two or more identified data sets; and

an alignment module arranged to distort one, or both, of data and its graphical representation associated with at least one of the identified data sets to map a first identified data set of a first periodicity onto a single undistorted identified data set of a second periodicity and align the identified periodic event instances relative to each other.

147. The system of claim 146, wherein the periodicity determination module is further arranged to

identify a first instance of the periodic event within a first data set within a first group of data sets having a first periodicity,

identify a second instance of the periodic event within a second data set within a second group of data sets having a first periodicity, and

determine whether the first data set is in a same or different relative position within the first group to the position of the second data set in the second group, and the alignment module is further arranged to align one or both of the data and its graphical representation associated with the first and second instances of the periodic event according to the position determination output by the periodicity determination module.

148. A temporal query system for constructing queries against data sets having different periodicities, the system comprising

a determination module arranged to determine the periodicity of the data sets,

a query resolving module arranged to resolve the temporal parameters passed in the query;

and

a data set creation module arranged to create data sets according to the resolved parameters by mapping a first identified data set of a first periodicity onto a single undistorted identified data set of a second periodicity.