Presentation on theme: "1 Presented by: Victor Gonzalez-Castro Lachlan MacKinnon A survey “Off the Record” – Using Alternative Data Models to Increase Data Density in Data Warehouse."— Presentation transcript:
1 Presented by: Victor Gonzalez-Castro Lachlan MacKinnon A survey “Off the Record” – Using Alternative Data Models to Increase Data Density in Data Warehouse Enviroments.
2 Agenda Introduction Data Sparsity State of the art Relational Model The Triple Store The Binary Model The Associative model The Transrelational model Our proposal Questions
3 Introduction In Data Warehouse environments Data Sparsity is a common issue that remains unresolved. Alternative Data Models that abandon the traditional record storage/manipulation structure have been researched. We are investigating the use of these alternative data models to increase data density with the idea to decrease data sparsity.
4 Origin of Data Sparsity Data sparsity is originated from the aim of answering all possible user queries from the information stored in a Data Warehouse that contains Nulls. $ $ $ $ $ $ $ $ $ $ $ $ $ Time Dimension Month Year Day Fig.1. A three level dimension and Nulls. After 
5 Origin of Data Sparsity (Cont…) Data Sparsity is the result of the Cartesian product of all dimensions and all aggregation levels. (Sparse) (Dense) Fig.2. Data Sparsity and data density. From .
6 State of the art. (Relational) The Relational Model  uses the traditional record storage/manipulation structure. 1234NutRedLondon It is the base model against which the other models will be compared. All RDBMS made a poor management of sparsity (missing information). Codd  suggested a fundamental change in the relational Model V2, the use of a 4 value-logic. No one has implemented this fundamental change
7 State of the art. (Relational) Major players on the Relational Market / SQL Server
8 State of the art. (TripleStore) IdentifierName 1Nut 2Red 3London …… The Triple Store. ,. It uses a Structure called the Name Store to keep all the names.,. To construct the processing Structure, uses Triples. 123 456 ……… lmn
9 State of the art. (TripleStore) The major project in Triple Store is TriStarp Tristarp was stablished in 1984. Leaded by Peter King with Support from IBM Hursley labs. Dr. Sharman from IBM Hursley  is visiting the Tristarp team. Current directions Further development of the persistent Triple Store Repository. Continuing Research on the graph-based model. Extending technology to manage partially structured data
10 State of the art. (Binary) SurPnameColorCity s1NutRedLondon s2BoltGreenParis s3ScrewBlueOslo The Binary Model  considers that all tables are Binary tables. SurCity s1London s2Paris s3Oslo SurPname s1Nut s2Bolt s3Screw SurColor s1Red s2Green s3Blue
11 State of the art. (Binary) A Major Project in the Binary Model  is MONETDB. Is a DBMS designed to provide high performance on complex queries against real-world sized database. Achieves this goal using innovations at all layers of a DBMS: a storage model based on vertical fragmentation, processing speed by self-tuning relational operators, algorithms designed to exploit modern hardware, self- managing indexing structures, modular and extensible software architecture, etc. It is developed at the Institute for Mathematics and Computer Science Research of The Netherlands.
12 State of the art. (Associative) IdentifierName 77Nut 08Red 32London 12That is 67Is located in The Associative Model  comprises two types of data structures Items and Links.[3 It differs from Binary and Triple store in one fundamental way; Associations themselves may be either the source or the target of other associations. It uses Quadruplets. IdentifierSourceVerbTarget 74771208 03746732
13 State of the art. (Associative) The Major product in the Associative Model is SentencesDB. Instead of using a separate, unique table for every different type of data, it uses a single, generic structure to contain all types of data. Information about the logical structure of the data and the rules that govern it are stored alongside the data in the database. The programs are truly reusable, and no longer need to be amended when the data structures change.
14 State of the art. (Transrelational) The TransRelational Model TM.  keeps the Relational model itself but abandon the record storage structure. It uses two structures: The Record Reconstruction Table. The Field Values Table. Since there is currently no instantiation of the Transrelational Model available, We will build an implementation of the essential algorithms.algorithms P#PNAMECOLORCITY P1BoltBlueLondon P2CamBlueLondon P3CogGreenLondon P4NutRedOslo P5ScrewRedParis P6ScrewRedParis P#PNAMECOLORCITY 4321 1144 5656 6413 2232 3565
15 TransrelationalTransrelational. Algorithms TransrelationalTransrelational. Algorithms P#PNAMECOLORCITY P1NutRedLondon P2BoltGreenParis P3ScrewBlueOslo P4ScrewRedLondon P5CamBlueParis P6CogRedLondon P#PNAMECOLORCITY 4321 1144 5656 6413 2232 3565 P#PNAMECOLORCITY P1BoltBlueLondon P2CamBlueLondon P3CogGreenLondon P4NutRedOslo P5ScrewRedParis P6ScrewRedParis Field Values Table (FVT) 1. A file for the suppliers relation 2. Sort each column in asc. Record Reconst. Table (RRT) P#PNAMECOLORCITY P1London NutRed 1. Go to Cell [1,1] of the FVT, fetch the value stored (P1). 3. Go to the corresponding RRT cell [4,2] and fetch the row number (4). The next (3 rd or COLOR) is the 4 th row in the FVT (Red). 5. Go to the corresponding RRT cell [4,1] and fetch value (1). The next 5 th column does not exist, so it wraps around to the 1 st column, so then is the 1 st row in the FVT. 4. Go to the corresponding RRT cell [4,3] and fetch value (1). The next 4 th or CITY) is the 1 st row in the FVT (London). 2. Go to the same cell [1,1] in the RRT and fetch the value (4). It is interpreted to mean that the next field value (PNAME), is in the 4 th row of the FVT. Go to that cell and fetch the value (Nut)
16 Alternative Data Models Comparison ModelStorage StructureLinkage Structure RelationalTable (Relation)By position Triple StoreName StoreTriple Store BinaryBinary TableJoins AssociativeItemsLinks TransrelationalField Values TableRecord Reconstruction Table
17 Our proposal (Our aims) To carry out an impartial survey on alternative Data Models. Compare whether or not the use of alternative data models can improve the Data Density in Data Warehouse environments. Observe the effect that such data density increase has on the data sparsity.
18 Our proposal (How…) We intend to use an implementation of each data model TransRelational TM We will use TPC-H data set to load each database. Run a set of benchmark metrics, where available if not we will develop our metrics to determine relative performance and then consider relative data density and sparsity.
19 Just Remember… Instead of storing data horizontally, do it vertically and eliminate duplicate values. 123 456 789 234 567 Bolt Screw Nut Nail Black Blue White Paris London Here are the Savings We are abandoning the traditional Record Structure, we are going “off the record”.
23 P1BoltBlueLondon P2CamBlueLondon P3CogGreenLondon P5ScrewRedParis P4NutRedOslo Just Remember… Instead of store data horizontally, do it vertically and eliminate duplicate values. P6ScrewRedParis
24 Just Remember… Instead of storing data horizontally, do it vertically and eliminate duplicate values. P1 P2 P3 P4 P5 P6 Bolt Cam Cog Nut Screw Blue Green Red London Oslo Paris Here are the Savings We are abandoning the traditional Record Structure, we are going “off the record”. firstname.lastname@example.org@macs.hw.ac.uk