1. A composition of matter comprising a hot melt adhesive comprising a metathesized unsaturated polyol ester wax.
2. The composition of claim 1, wherein the hot melt adhesive composition is a conventional hot melt adhesive.
3. The composition of claim 1, wherein the hot melt adhesive composition is a hot melt pressure-sensitive adhesive.
4. The composition of claim 1, wherein the hot melt adhesive composition is a low application temperature adhesive.
5. The composition of claim 1, wherein the hot melt adhesive is a bottle labeling adhesive.
6. The composition of claim 1, wherein the metathesized unsaturated polyol ester wax is partially hydrogenated.
7. The composition of claim 1, wherein the metathesized unsaturated polyol ester wax is fully hydrogenated.
8. The composition of claim 6, wherein the partially hydrogenated metathesized unsaturated polyol ester wax has an iodine value of about 120 or less.
9. The composition of claim 7, wherein the fully hydrogenated metathesized unsaturated polyol ester wax has an iodine value of about 10 or less.
10. The composition of claim 1, wherein the metathesized unsaturated polyol ester wax comprises a metathesized natural oil.
11. The composition of claim 10, wherein the metathesized natural oil comprises metathesized vegetable oil or metathesized animal fat.
12. The composition of claim 11, wherein the metathesized vegetable oil is selected from the group consisting of metathesized soybean oil, metathesized canola oil, metathesized rapeseed oil, metathesized coconut oil, metathesized corn oil, metathesized cottonseed oil, metathesized olive oil, metathesized palm oil, metathesized peanut oil, metathesized safflower oil, metathesized sesame oil, metathesized soybean oil, or metathesized sunflower oil.
13. The composition of claim 1, wherein the metathesized unsaturated polyol ester wax comprises dimers, trimers, tetramers, and higher order oligomers.
14. The composition of claim 2, wherein the hot melt adhesive comprises:
(a) a wax comprising a hydrogenated metathesized unsaturated polyol ester;
(b) a tackifier;
(c) a synthetic or petroleum-derived wax; and
(d) an ethyl vinyl acetate polymer.
15. The composition of claim 14, wherein the synthetic or petroleum-derived wax comprises paraffin wax, microcrystalline wax, polyethylene wax, or Fischer-Tropsch wax.
16. The composition of claim 14, wherein the tackifier comprises one or more of a rosin ester tackifier or a terpene-phenolic tackifier.
17. The composition of claim 3, wherein the hot melt pressure-sensitive adhesive comprises:
(a) a wax comprising a hydrogenated metathesized unsaturated polyol ester;
(b) a tackifier; and
(c) a polymer.
18. The composition of claim 17, wherein the polymer comprises a styrene-isoprene block copolymer.
19. An article comprising a substrate having a surface that is at least partially coated with a hot melt pressure-sensitive adhesive comprising a metathesized unsaturated polyol ester wax.
20. The article of claim 19, wherein the article comprises:
(a) a substrate at least partially coated with a layer of a pressure-sensitive adhesive comprising a metathesized unsaturated polyol ester wax; and
(b) a release liner comprising uncoated polyester film, wherein an uncoated surface of the release liner is in direct contact with the layer of pressure-sensitive adhesive.
21. The article of claim 19, wherein the substrate comprises paper or cardboard.
22. The article of claim 19, wherein the article is a paper label.
23. The article of claim 19, wherein the pressure-sensitive adhesive is removable.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method, in a data processing system, for transforming an original graph data set into a representative form having a smaller number of dimensions than that of the original graph data set, comprising:
generating, in a processor of the data processing system, a graph transformation basis structure based on an input graph data structure;
transforming, by the processor, an original graph data set based on an intersection of the graph transformation basis structure and the input graph data structure to thereby generate a transformed graph data set data structure, wherein the transformed graph data set data structure has a reduced dimensionality from that of the input graph data structure but represents characteristics of the original graph data set; and
performing, by the processor or a different processor of the data processing system, an application specific operation on the transformed graph data set data structure to generate an output of a closest similarity record in the transformed graph data set to a target component, wherein the graph transformation basis structure is a set of concept graphs having disjointed edge sets from one another, and wherein individual graphs within the graph transformation basis structure have coordinates along a concept graph in the set of concept graphs defined by a sum of corresponding edge frequencies of the concept graph along an edge of the individual graph.
2. The method of claim 1, wherein the input graph data structure has a size that exceeds an available storage capacity of a system memory of the data processing system.
3. The method of claim 1, wherein the input graph data structure comprises data sets for a plurality of graphs.
4. The method of claim 1, wherein transforming an original graph data set based on an intersection of the graph transformation basis structure and the input graph data set to thereby generate a transformed graph data set data structure comprises:
performing, for each graph in the input graph data structure, a dot product operation on the graph transformation basis structure and the graph of the input graph data structure to generate a coordinate representation of the graph; and
storing the resulting coordinate representation of the graphs of the input graph data structure.
5. The method of claim 1, wherein performing an application specific operation on the transformed graph data set data structure comprises:
receiving the target component;
generating an inverted list of record identifiers for transformed coordinates in the transformed graph data set data structure;
generating, for each dimension of the transformed graph data set data structure, a list of records for which a corresponding coordinate value, in the transformed graph data set data structure, is non-zero;
computing a similarity of the target component to records in the list of records for each of the dimensions of the transformed graph data set data structure; and
reporting a closest record based on the similarity of the target component to the records in the lists of records.
6. A method, in a data processing system, for transforming an original graph data set into a representative form having a smaller number of dimensions than that of the original graph data set, comprising:
generating, in a processor of the data processing system, a graph transformation basis structure based on an input graph data structure;
transforming, by the processor, an original graph data set based on an intersection of the graph transformation basis structure and the input graph data structure to thereby generate a transformed graph data set data structure, wherein the transformed graph data set data structure has a reduced dimensionality from that of the input graph data structure but represents characteristics of the original graph data set; and
performing, by the processor or a different processor of the data processing system, an application specific operation on the transformed graph data set data structure to generate an output of a closest similarity record in the transformed graph data set to a target component, wherein generating a graph transformation basis structure based on an input graph data structure comprises:
sampling edges from the input graph data structure;
generating components of a basis structure based on components in the input graph data structure connected by the sampled edges;
repeating the sampling and generating operations to generate a plurality of basis structures; and
selecting a basis structure from the plurality of basis structures to be used as the graph transformation basis structure.
7. The method of claim 6, wherein each basis structure in the plurality of basis structures is different from other basis structures in the plurality of basis structures.
8. A computer program product comprising a non-transitory computer readable medium having a computer readable program recorded thereon, wherein the computer readable program, when executed on a computing device, causes the computing device to:
generate a graph transformation basis structure based on an input graph data structure;
transform an original graph data set based on an intersection of the graph transformation basis structure and the input graph data structure to thereby generate a transformed graph data set data structure, wherein the transformed graph data set data structure has a reduced dimensionality from that of the input graph data structure but represents characteristics of the original graph data set; and
perform an application specific operation on the transformed graph data set data structure to generate an output of a closest similarity record in the transformed graph data set to a target component, wherein the graph transformation basis structure is a set of concept graphs having disjointed edge sets from one another, and wherein individual graphs within the graph transformation basis structure have coordinates along a concept graph in the set of concept graphs defined by a sum of corresponding edge frequencies of the concept graph along an edge of the individual graph.
9. The computer program product of claim 8, wherein the input graph data structure has a size that exceeds an available storage capacity of a system memory of the data processing system.
10. The computer program product of claim 8, wherein the input graph data structure comprises data sets for a plurality of graphs.
11. The computer program product of claim 8, wherein the computer readable program causes the computing device to transform an original graph data set based on an intersection of the graph transformation basis structure and the input graph data set to thereby generate a transformed graph data set data structure by:
performing, for each graph in the input graph data structure, a dot product operation on the graph transformation basis structure and the graph of the input graph data structure to generate a coordinate representation of the graph; and
storing the resulting coordinate representation of the graphs of the input graph data structure.
12. The computer program product of claim 8, wherein the computer readable program causes the computing device to perform an application specific operation on the transformed graph data set data structure by:
receiving the target component;
generating an inverted list of record identifiers for transformed coordinates in the transformed graph data set data structure;
generating, for each dimension of the transformed graph data set data structure, a list of records for which a corresponding coordinate value, in the transformed graph data set data structure, is non-zero;
computing a similarity of the target component to records in the list of records for each of the dimensions of the transformed graph data set data structure; and
reporting a closest record based on the similarity of the target component to the records in the lists of records.
13. A computer program product comprising a non-transitory computer readable medium having a computer readable program recorded thereon, wherein the computer readable program, when executed on a computing device, causes the computing device to:
generate a graph transformation basis structure based on an input graph data structure;
transform an original graph data set based on an intersection of the graph transformation basis structure and the input graph data structure to thereby generate a transformed graph data set data structure, wherein the transformed graph data set data structure has a reduced dimensionality from that of the input graph data structure but represents characteristics of the original graph data set; and
perform an application specific operation on the transformed graph data set data structure to generate an output of a closest similarity record in the transformed graph data set to a target component, wherein the computer readable program causes the computing device to generate a graph transformation basis structure based on an input graph data structure by:
sampling edges from the input graph data structure;
generating components of a basis structure based on components in the input graph data structure connected by the sampled edges;
repeating the sampling and generating operations to generate a plurality of basis structures; and
selecting a basis structure from the plurality of basis structures to be used as the graph transformation basis structure.
14. The computer program product of claim 13, wherein each basis structure in the plurality of basis structures is different from other basis structures in the plurality of basis structures.
15. An apparatus, comprising:
a processor; and
a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to:
generate a graph transformation basis structure based on an input graph data structure;
transform an original graph data set based on an intersection of the graph transformation basis structure and the input graph data structure to thereby generate a transformed graph data set data structure, wherein the transformed graph data set data structure has a reduced dimensionality from that of the input graph data structure but represents characteristics of the original graph data set; and
perform an application specific operation on the transformed graph data set data structure to generate an output of a closest similarity record in the transformed graph data set to a target component, wherein the graph transformation basis structure is a set of concept graphs having disjointed edge sets from one another, and wherein individual graphs within the graph transformation basis structure have coordinates along a concept graph in the set of concept graphs defined by a sum of corresponding edge frequencies of the concept graph along an edge of the individual graph.
16. The apparatus of claim 15, wherein the instructions cause the processor to transform an original graph data set based on an intersection of the graph transformation basis structure and the input graph data set to thereby generate a transformed graph data set data structure by:
performing, for each graph in the input graph data structure, a dot product operation on the graph transformation basis structure and the graph of the input graph data structure to generate a coordinate representation of the graph; and
storing the resulting coordinate representation of the graphs of the input graph data structure.
17. The apparatus of claim 15, wherein the instructions cause the processor to perform an application specific operation on the transformed graph data set data structure by:
receiving the target component;
generating an inverted list of record identifiers for transformed coordinates in the transformed graph data set data structure;
generating, for each dimension of the transformed graph data set data structure, a list of records for which a corresponding coordinate value, in the transformed graph data set data structure, is non-zero;
computing a similarity of the target component to records in the list of records for each of the dimensions of the transformed graph data set data structure; and
reporting a closest record based on the similarity of the target component to the records in the lists of records.
18. An apparatus, comprising:
a processor; and
a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to:
generate a graph transformation basis structure based on an input graph data structure;
transform an original graph data set based on an intersection of the graph transformation basis structure and the input graph data structure to thereby generate a transformed graph data set data structure, wherein the transformed graph data set data structure has a reduced dimensionality from that of the input graph data structure but represents characteristics of the original graph data set; and
perform an application specific operation on the transformed graph data set data structure to generate an output of a closest similarity record in the transformed graph data set to a target component, wherein the instructions cause the processor to generate a graph transformation basis structure based on an input graph data structure by:
sampling edges from the input graph data structure;
generating components of a basis structure based on components in the input graph data structure connected by the sampled edges;
repeating the sampling and generating operations to generate a plurality of basis structures; and
selecting a basis structure from the plurality of basis structures to be used as the graph transformation basis structure.