1. A catalyst suitable for isomerization of xylenes and conversion of ethylbenzene comprising a catalytically-effective amount of molecular sieve having a pore diameter of from about 4 to 8 angstroms and a catalytically-effective amount of molybdenum hydrogenation component in the presence of sufficient amorphous aluminum phosphate binder to provide less than about 3 mass-% net toluene and trimethylbenzene make (based on total C8 aromatics in the feed) at 75% ethylbenzene conversion under Evaluation Conditions.
2. The catalyst of claim 1 wherein the molecular sieve is a pentasil molecular sieve.
3. The catalyst of claim 2 wherein the molecular sieve comprises MFI-type molecular sieve.
4. The catalyst of claim 3 wherein the catalyst comprises 2 to 90 mass-% molecular sieve.
5. The catalyst of claim 4 wherein the catalyst comprises 0.1 to 5 mass-% molybdenum (elemental basis).
6. The catalyst of claim 1 wherein the Al:P atomic ratio in the aluminum phosphate binder is from about 1:10 to 10:1.
7. The catalyst of claim 1 wherein the catalyst comprises 20 to 500 ppm-mass platinum.
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 computer system for accessing information, comprising:
memory having stored therein computer-executable instructions; and
a processor executing said computer-executable instructions including:
establishing a row store database for storing data, wherein each row of said row store database comprises a plurality of attributes, wherein said data in said row store database is current to a temporal point in time;
establishing a column store database comprising data structured to satisfy received analytic queries; and
beginning from an initial state of said column store database, for each subsequently received analytic query, importing a targeted amount of data from a corresponding temporal state of said row store database into said column store database to satisfy said corresponding subsequently received analytic query.
2. The computer system of claim 1, wherein said importing a targeted amount of data of said computer-executable instructions comprises:
receiving a first analytic query, wherein said first analytic query is directed to a queried range of primary key attributes for a referenced attribute vector corresponding to a referenced attribute in said plurality of attributes;
determining if a covered range of said primary key attributes in said referenced attribute vector is within said queried range of primary key attributes; and
when said covered range is not within said queried range, importing said targeted amount of data from said row store database during execution of said first analytic query to enhance said covered range in order to encompass said queried range.
3. The computer system of claim 2, wherein said determining if a covered range of said computer-executable instructions comprises:
accessing metadata comprising information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.
4. The computer system of claim 1, wherein said computer-executable instructions as executed by said processor further comprises:
updating metadata to reflect said targeted amount of data, wherein said metadata comprises information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.
5. The computer system of claim 1, wherein said importing data of said computer-executable instructions comprises:
performing a full table scan of said row store database to access said targeted amount of data.
6. The computer system of claim 1, wherein said importing data of said computer-executable instructions comprises:
performing a partial table scan of said row store database to access said targeted amount of data.
7. The computer system of claim 1, wherein said computer-executable instructions as executed by said processor further comprises:
persistently storing said column store database updated with said targeted amount of data to disk.
8. A method for dynamically building a column store database from a row store database, comprising;
establishing said row store database for storing data, wherein each row of said row store database comprises a plurality of attributes, wherein said data in said row store database is current to a temporal point in time;
establishing said column store database comprising data structured to satisfy received analytic queries; and
beginning from an initial state of said column store database, for each subsequently received analytic query, importing a targeted amount of data from a corresponding temporal state of said row store database into said column store database to satisfy said corresponding subsequently received analytic query.
9. The method of claim 8, wherein said importing a targeted amount of data comprises:
receiving a first analytic query, wherein said first analytic query is directed to a queried range of primary key attributes for a referenced attribute vector corresponding to a referenced attribute in said plurality of attributes;
determining if a covered range of said primary key attributes in said referenced attribute vector is within said queried range of primary key attributes; and
when said covered range is not within said queried range, importing said targeted amount of data from said row store database during execution of said first analytic query to enhance said covered range in order to encompass said queried range.
10. The method of claim 9, wherein said determining if a covered range comprises:
accessing metadata comprising information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.
11. The method of claim 8, further comprising:
updating metadata to reflect said targeted amount of data, wherein said metadata comprises information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.
12. The method of claim 8, wherein said importing data comprises:
performing a full table scan of said row store database to access said targeted amount of data.
13. The method of claim 8, wherein said importing data comprises:
performing a partial table scan of said row store database to access said targeted amount of data.
14. The method of claim 8, further comprising:
persistently storing said column store database updated with said targeted amount of data to disk.
15. A non-transitory computer-readable medium having computer-executable instructions for causing a computer system to perform a method, comprising:
establishing said row store database for storing data, wherein each row of said row store database comprises a plurality of attributes, wherein said data in said row store database is current to a temporal point in time;
establishing said column store database comprising data structured to satisfy received analytic queries; and
beginning from an initial state of said column store database, for each subsequently received analytic query, importing a targeted amount of data from a corresponding temporal state of said row store database into said column store database to satisfy said corresponding subsequently received analytic query.
16. The computer-readable medium of claim 15, wherein said importing a targeted amount of data in said method comprises:
receiving a first analytic query, wherein said first analytic query is directed to a queried range of primary key attributes for a referenced attribute vector corresponding to a referenced attribute in said plurality of attributes;
determining if a covered range of said primary key attributes in said referenced attribute vector is within said queried range of primary key attributes; and
when said covered range is not within said queried range, importing said targeted amount of data from said row store database during execution of said first analytic query to enhance said covered range in order to encompass said queried range.
17. The computer-readable medium of claim 16, wherein said determining if a covered range in said method comprises:
accessing metadata comprising information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.
18. The computer-readable medium of claim 15, wherein said method further comprises:
updating metadata to reflect said targeted amount of data, wherein said metadata comprises information related to each attribute vector in said column store database and a covered range of primary keys corresponding to each attribute vector in said column store database.
19. The computer-readable medium of claim 15, wherein said importing data in said method comprises:
performing a full table scan of said row store database to access said targeted amount of data.
20. The computer-readable medium of claim 15, wherein said importing data in said method comprises:
performing a partial table scan of said row store database to access said targeted amount of data.