lib/WebPAC/Manual.pod

=head1 WebPAC - Search engine or data-warehouse manual

It's quite hard to explain conceisly what webpac is. It's a mix between
search engine and data warehousing application. Let's see that in detail...

WebPAC was originally written to search CDS/ISIS records using C<swish-e>.
Since then it has, however, adopted different other input formats and added
support for alphabetical lists (earlier described as indexes).

With evolution of this concept, we decided to produce following work-flow
of your data:

  step

   source file          CDS/ISIS, MARC, Excel, robots, ...
      |
  1   | apply import normalisation rules (xml)
      V
   intermidiate         this data is re-formatted source data converted
     data               to chunks based on tag names from import_xml
      |
  2   | apply output filter (TT2)
      V
     data               search engine, HTML, OAI, RDBMS
      |
  3   | filter using query in REST format
  4   | apply output filter (TT2)
      V
    client              Web browser, SOAP

=head2 Normalisation and Intermidiate data

This is first step in working with your data.

You are creating mappings, one-to-one from source data records to documents
in webpac. You can split or merge data from input records, apply filters
(perl subroutines), use lookups within same source file or do simple
evaluations while producing output.

All that is controlled with C<import_xml> configuration file. You will want
to create fine-grained chunks of data (like separate first and last name),
which will later be used to produce output. You can think of conversation
process as application of C<import_xml> recepie on every input record.

Each tag within recepie is creating one new records as long as there are
fields in input format (which can be repeatable) that satisfy at least one
field within tag.

Users of older webpac should note that this file doesn't contain any more
formatting or specification of output type and that granularity of each tag
has increased.

=head2 Output filter

Now that we have normalized record, we can create some output. You can create
html from it, data files for search engine or insert them into RDBMS.

The twist is that application of output filters can be recursive, allowing
you to query data generated in previous step. This enables to you represent
lists or trees from source data that have structure. This also requires to
produce structured data in step 2 which can be filtered and queried in steps
3 and 4 to produce final output.

You should note that you can query intermidiate data in step 4 also, not
just data produced in step 2.

Output filter use Template Toolkit 2, so you have full power of simple
procedural language (loops, conditions) and handy built-in functions to
produce output.

=head2 REST Query Format

Design decision is to use REST query format. This has benefit of simplicity
and ability to create unique URLs to all content within webpac. Simple query
format is:

  http://webpac/search/html/personal_name/Joe%20Doe/AND/year/LT%201995

This REST query can be broken down to:

=over

=item http://webpac

Hostname on which service is running. Not required if doing lookups, just
for browser usage.

=item search

Name of output filtering methods. This will specify search engine.

=item html

Specified template that will be used to produce output.

=item perlsonal_name/Joe%20Doe...

URL encoded query string. It is specific to filtering method used.

=back

You can easily produce RSS feed for same query using follwing REST url:

  http://webpac/search/rss/personal_name/Joe%20Doe/AND/year/LT%201995

Yes, it really is that simple. As it should be.

=head1 Tehnical stuff

Following text will be more hard-code tehnical stuff about how is webpac
implemented and why.

=head2 Search Engine

We are using Hyper Estraier search engine using pgestraier PostgreSQL bindings
for it.

It should be relativly easy to plugin another one if need arise.

=head2 Data Warehouse

In a nutshell, webpac has evolved to support hybrid data as input. That
means it has become kind of data-warehouse application. It doesn't support
directly roll-up and roll-down operations, but they can be emulated using
intermidiate data step or output step.

1	dpavlin	1	=head1 WebPAC - Search engine or data-warehouse manual
2
3			It's quite hard to explain conceisly what webpac is. It's a mix between
4			search engine and data warehousing application. Let's see that in detail...
5
6			WebPAC was originally written to search CDS/ISIS records using C<swish-e>.
7			Since then it has, however, adopted different other input formats and added
8			support for alphabetical lists (earlier described as indexes).
9
10			With evolution of this concept, we decided to produce following work-flow
11			of your data:
12
13			step
14
15			source file CDS/ISIS, MARC, Excel, robots, ...
16			\|
17			1 \| apply import normalisation rules (xml)
18			V
19			intermidiate this data is re-formatted source data converted
20			data to chunks based on tag names from import_xml
21			\|
22			2 \| apply output filter (TT2)
23			V
24			data search engine, HTML, OAI, RDBMS
25			\|
26			3 \| filter using query in REST format
27			4 \| apply output filter (TT2)
28			V
29			client Web browser, SOAP
30
31			=head2 Normalisation and Intermidiate data
32
33			This is first step in working with your data.
34
35			You are creating mappings, one-to-one from source data records to documents
36			in webpac. You can split or merge data from input records, apply filters
37			(perl subroutines), use lookups within same source file or do simple
38			evaluations while producing output.
39
40			All that is controlled with C<import_xml> configuration file. You will want
41			to create fine-grained chunks of data (like separate first and last name),
42			which will later be used to produce output. You can think of conversation
43			process as application of C<import_xml> recepie on every input record.
44
45			Each tag within recepie is creating one new records as long as there are
46			fields in input format (which can be repeatable) that satisfy at least one
47			field within tag.
48
49			Users of older webpac should note that this file doesn't contain any more
50			formatting or specification of output type and that granularity of each tag
51			has increased.
52
53			=head2 Output filter
54
55			Now that we have normalized record, we can create some output. You can create
56			html from it, data files for search engine or insert them into RDBMS.
57
58			The twist is that application of output filters can be recursive, allowing
59			you to query data generated in previous step. This enables to you represent
60			lists or trees from source data that have structure. This also requires to
61			produce structured data in step 2 which can be filtered and queried in steps
62			3 and 4 to produce final output.
63
64			You should note that you can query intermidiate data in step 4 also, not
65			just data produced in step 2.
66
67			Output filter use Template Toolkit 2, so you have full power of simple
68			procedural language (loops, conditions) and handy built-in functions to
69			produce output.
70
71			=head2 REST Query Format
72
73			Design decision is to use REST query format. This has benefit of simplicity
74			and ability to create unique URLs to all content within webpac. Simple query
75			format is:
76
77			http://webpac/search/html/personal_name/Joe%20Doe/AND/year/LT%201995
78
79			This REST query can be broken down to:
80
81			=over
82
83			=item http://webpac
84
85			Hostname on which service is running. Not required if doing lookups, just
86			for browser usage.
87
88			=item search
89
90			Name of output filtering methods. This will specify search engine.
91
92			=item html
93
94			Specified template that will be used to produce output.
95
96			=item perlsonal_name/Joe%20Doe...
97
98			URL encoded query string. It is specific to filtering method used.
99
100			=back
101
102			You can easily produce RSS feed for same query using follwing REST url:
103
104			http://webpac/search/rss/personal_name/Joe%20Doe/AND/year/LT%201995
105
106			Yes, it really is that simple. As it should be.
107
108			=head1 Tehnical stuff
109
110			Following text will be more hard-code tehnical stuff about how is webpac
111			implemented and why.
112
113			=head2 Search Engine
114
115			We are using Hyper Estraier search engine using pgestraier PostgreSQL bindings
116			for it.
117
118			It should be relativly easy to plugin another one if need arise.
119
120			=head2 Data Warehouse
121
122			In a nutshell, webpac has evolved to support hybrid data as input. That
123			means it has become kind of data-warehouse application. It doesn't support
124			directly roll-up and roll-down operations, but they can be emulated using
125			intermidiate data step or output step.
126