Data Warehousing: the New Knowledge Management Architecture for Humanities Research?

Data Warehousing:the New Knowledge Management Architecture for Humanities Research? Janet Delve University of Portsmouth, UK UKAIS 2004

Introduction • Data Warehouses everywhere • Amazon • Wal*Mart • Opodo • DWs used a lot in industry, and scientific research, but not in humanities research. • Written paper covers linguistics and history. Talk covers history in detail and gestures towards linguistics.

Overview • Introduction • Data modelling and traditional databases • Source-oriented data modelling • Data Mining • Philosophy of data warehousing • Background of DWs • Basic components of a data warehouse (DW) • Advantages of DWs • Findings –Humanities and DWs • Humanities and DWs – some issues • Examples of possible Humanities DWs • Ideas for the future?

Data Modelling • Relational data modelling – material split into many tables in order to gain enhanced performance – no duplication, updating or insertion anomalies etc. • Source-oriented data modelling – emphasis on modelling data as closely as possible to original source which is included in its entirety for posterity. • DW data modelling nearer to source-oriented approach in spirit.

Traditional databases • ERD p117 Harvey and Press

Traditional databases • Harvey and Press p.129

Historical Data • This can be difficult to model because: • It is irregular in structure, • It is complex • It is erratic in terms of when it occurs • Using a relational database can mean data from a single source being spit into many tables.

Source-oriented data modelling • ‘a semantic network tempered by hierarchical considerations’ [Thaller 1991, 155]. • Its flexible nature gives kleiw a ‘rubber band data structures’ facility [Denley 1994, 37]. • The fluid nature of creating a database with kleiw marks it out as an ‘organic’ DBMS.

Data Mining • The whole field is often referred to as data mining, which is also a major component within the field. • Data mining (DM) is normally used on large quantities (terabytes) of data, to find meaningful patterns. Neural nets, statistical modelling, decision trees are just some AI methods used. SQL can be used too. Parallel data processing is used with DM. • In order to mine data, it must be kept in a suitable system - a data warehouse is ideal.

Philosophy of data warehousing • ‘Data warehousing is an architecture, not a technology. There is the architecture, and there is the underlying technology, and they are two very different things. Unquestionably there is a relationship between data warehousing and database technology, but they are most certainly not the same. Data warehousing requires the support of many different kinds of technology.’ • Inmon 2002

Background of DWs • Business-oriented – serve the analytical needs of a company. The ordinary DBMS is still needed for the day-to-day queries, and also to feed the DW. • W.H. Inmon, father of DW. Cabinet effect –1991 • R. Kimball, expert on dimensional modelling • Need for single, integrated source of clean data, particularly for multinational etc. companies • Supporting technology from e.g. Oracle, Prism Solutions, IBM

Data Marts • Data marts contain DW data but are restricted to one department or one business process. • The industry is divided about data marts, • Inmon recommends building the DW first, then siphoning off the data to data marts. • Kimball believes you should build several data marts first, then integrate them into a DW.

Basic components of a Data Warehouse (DW) • A DW is subject-oriented, integrated, non-volatile & time-variant. • The major subjects for an insurance company are customer, policy, premium and claim. Previously data modelled around applications -car, health, life and accident. • Integration is the most important facet of a DW. Previous inconsistencies are ironed out and all data unambiguously entered into DW. Many sources of data can be placed in DW.

Basic components of a Data Warehouse (DW) • Non-volatile data in a DW means that it is not changed in the way data is in operational database – data is loaded en masse and isn’t updated. Obviates need for normalisation. • Time- variant – DW time horizon 5 –10 years, operational database 2-3 months. DW snapshots, operational database current data, DW always has element of time, operational database may or may not have. Inmon 2002

Basic components of a Data Warehouse (DW) • Kimball p7

Typical Architecture of a Data Warehouse

Meta Data • Meta data is extremely important in a DW. It is used: • to log the extraction and loading of data into the warehouse; • in query management to locate the most appropriate data source and also to help end users to build queries; • to show how the data has been mapped when carrying out data cleansing and transformations; • To manage all the data in the DW – recording where data came from, when etc.

Basic components of a Data Warehouse (DW) • Fact Tables • ‘A fact table is the primary table in a dimensional model where the numerical performance measurements of the business are stored… • The measurement data resulting from a business process is stored in a single data mart • Since measurement data is overwhelmingly the largest part of any data mart, we avoid duplicating it in multiple places around the enterprise’ Kimball 2002

Basic components of a Data Warehouse (DW) • Dimension tables • These contain the textual descriptors of the business. Their depth and breadth define the usefulness of the DW. • Contains data that doesn’t change frequently • Can have 50-100 attributes. • Not usually normalized. (Snowflake and starflake) • Coding disparaged (Long term view)

Basic components of a Data Warehouse (DW) • Star schema Kimball p51

Data Warehousing Tools and Technologies • Building a data warehouse is a complex task because there is no vendor that provides an ‘end-to-end’ set of tools. • Necessitates that a data warehouse is built using multiple products from different vendors. • Ensuring that these products work well together and are fully integrated is a major challenge.

Advantages of DWs • Flexibility in modelling data. • Time dimension – country-specific calendars and synchronization across multiple time zones. • Easy to add external data and summarised data. • Built for analysis. • Built for huge volumes of data (terabytes of data – a trillion 1012). • Can cope with ‘idiosyncrasies of geographic location dimensions’ within GISs.

Possible advantages of DWs • Indexing facilities of DW. • Publishing the ‘right data’ – data collected from a variety of sources and edited for quality and consistency. • DW seeks to collate all data so a variety of different subsets can be analysed whenever required. • Easy to extend DW and add material from a new source. • Data cleansing techniques. • Tracking facility afforded by meta data

Disadvantages of DWs • Some humanities data fits into the ‘numerical fact’ topology, some doesn’t • Technology not easy and is based on having existing databases to extract from • Regular snapshots not the same but they could equate to data sets taken at different periods of time (e.g. 1841 census, 1861 census) • A lot to learn.

Findings – Humanities and DWs • NAGARA • (National Association of Government Archives and Records Administrators) • Article on DWs by Mary Klauda of the Minnesota Historical Society 1999 (archivist) • Eastern Connecticut schools DW 2002 • Bo Wandschneider – University of Guelph, Canada -DW and the use of census data. ICPSR (Inter-university Consortium for Political and Social Research)

Findings – Humanities and DWs • University of California DW – memo to Humanities department • Social Science DW – Human Resources DW project of Human Sciences Research Council, South Africa • GEOBASE, Israel. DW of Israel’s regional statistics, supported by National Planning Authority in the Ministry of Interior Affairs.

Humanities and DWs – some issues • Scale – can cope with really large country / state -wide problems. • Can analyse e.g. British censuses 1841-1901 (108). • Can put several databases together to produce a time run – e.g Hearth taxes, window taxes, poll taxes, land taxes, poor rates all in one DW. • Oracle site licenses.

Examples of possible History DWs

Examples of possible History DWs HOLDING DETAILS ----------------------------- Holding ID King Tenant in Chief Manor Lord VILL Etc. PROPERTY INFORMATION -------------------------------- Property Id Property description Property value Etc MANOR ---------------------------- ManorId Holding Id Property Id Original Owner Id Date Manor Value Tax (Hides) Cottar Population Bordar Population Villein Population Sokeman Population Pries Population Number of Burgesses Number of slaves Etc. ORIGINAL OWNER ---------------------------- Original Owner ID Etc.

Examples of possible History DWs • Data from a variety of sources over time– hearth tax, poor rates, trade directories, census, street directories, wills and inventories, GIS maps for a city e.g. Winchester. • Voting data – poll book data and rate book data up to 1870 for whole country (note some data missing). • Port data – all data from portbooks for all British ports together with yearly trade figures. • Street directories for whole country for last 100 years. • Taxation overview – different types / areas / periods.

Examples of possible History DWs • 19th C British census data doesn’t fit into the typical DW model as it doesn’t have the numerical facts to go into a fact table. • However, there’s a recent development in DWs – ‘factless’ fact tables. • There is real scope to be able to model historical data using these.

Examples of possible History DWs • Kimball p247

Examples of possible Humanities DWs • Language DW – could contain databases of different languages for comparison, or many databases of same languages over larger area. • DW of worldwide scholarly community / whole culture • GIS or archaeological DW by continent etc. rather than country. • DW of biographies. • DW of library catalogues or archives for enhanced public access.

Ideas for the future? • Instead of ‘me and my database’ - emphasis on smallish, individual, national projects, • Maybe • ‘Our integrated warehouse’ – emphasis on large scale, collaborative, international projects?

Data Warehousing: the New Knowledge Management Architecture for Humanities Research?