This document describes the process of migrating data from the proprietary Libsys ILMS to the open source Koha ILMS at the National Institute of Science Communication and Information Resources (NISCAIR) library in India. Key steps included:
1) Generating reports from Libsys and converting them to Excel files
2) Cleaning the data by removing blanks and consolidating multi-line fields
3) Converting the Excel files to MARC format files
4) Importing the MARC files into Koha
5) Customizing Koha's interface, administration tools, and OPAC to meet NISCAIR's needs.
LIBSYS Ltd. is a pioneer in library automation software in India; the company provides flexible, automated, and innovatory library management solutions and brings a high level of accuracy in the services it offers. Since its inception in 1984, the company has been providing robust and quality services to libraries in different parts of the country. It strives for continuous innovation by incorporating latest technology in its products and services.
Presented at the seminar Libraries and the Semantic Web: the role of International Standard Bibliographic Description (ISBD), National Library of Scotland, Edinburgh, 25 Feb 2011
National Library Week Celebration, Workshop on Koha.
Venue: Mahatma Gandhi University Library
Organised by
Kerala Library Association
Kottayam Region
&
Mahatma Gandhi University Library
Kottayam
LIBSYS Ltd. is a pioneer in library automation software in India; the company provides flexible, automated, and innovatory library management solutions and brings a high level of accuracy in the services it offers. Since its inception in 1984, the company has been providing robust and quality services to libraries in different parts of the country. It strives for continuous innovation by incorporating latest technology in its products and services.
Presented at the seminar Libraries and the Semantic Web: the role of International Standard Bibliographic Description (ISBD), National Library of Scotland, Edinburgh, 25 Feb 2011
National Library Week Celebration, Workshop on Koha.
Venue: Mahatma Gandhi University Library
Organised by
Kerala Library Association
Kottayam Region
&
Mahatma Gandhi University Library
Kottayam
when new subject come into existence ,we have to give a place among already existing subject. this ppt will help to how can we assign a place to particular subject.it will helpful for all the students whom are pursuing their master in library science ans information management
Introduction to MARC
History (MARC to MARC 21)
Why MARC 21/Need of MARC 21
Characteristics
Design principle for MARC 21
MARC 21 Documentation
MARC 21Record System
MARC 21 Communication formats
MARC 21 Format for Bibliographic Data
Component of bibliographic record
Communication Standard
Mapping of MARC 21
MARC 21 Translation
Maintenance Agency
MARC 21 Regulation
Advantage of MARC 21
Problems with MARC 21
Future of MARC 21
A comparative analysis of library classification systemsAli Hassan Maken
We use classification each & every moment of the life by intentionally or unintentionally. Classification has always been the backbone of all Library operations and without it, library is definitely going to suffer in its recourse and to find a particular piece of information from unorganized heap of knowledge is almost impossible. The library classification is core instrument for organizing and retrieval of the documents stored in a library. At present era they are the navigation tools for locating and retrieving documents in more precisely and relevantly. The electronic versions of the DDC and UDC and other classification schemes make it possible to realize the potential of library classification to improve subject retrieval.
presentation on "CATALOGUING" during Training workshop in library science for staff of muktangan school libraries organised by muktangan school teacher reference library, mumbai on 15th November 2010
A presentation on Digital Library Software by Rupesh Kumar A, Assistant Professor, Department of Studies and Research in Library and Information Science, Tumkur University, Tumakuru, Karnataka, India.
Anglo-American Cataloguing Rules AACR2 to acquire an international adaptability.Cataloging & Classification.AACR1 and AACR2.AACR1 Anglo-American Cataloging Rules. North American text. Chicago: American Library Association, 1967.
AACR1, Chap. 12 Anglo-American Cataloging Rules. North American text. Chapter 12. Chicago: American Library
Association,
1975.
AACR2 Anglo-American Cataloguing Rules. 2nd ed. Chicago: American Library Association, 1
This PPT contain details of Z39.50 and useful for Library Science students. This protocol used for information retrieval and in the end list of different types of protocols are given.
Collection Development (that based on the five laws of S.R.Ranghanathan) is very important part of Collection Management. If we don’t adopt advanced technologies, collection then we can never fulfill the need of advanced users and libraries will become freeze, this is against the 5th law of Ranghanathan that “ Library is a growing organism”.
Big Data Everywhere Chicago: Apache Spark Plus Many Other Frameworks -- How S...BigDataEverywhere
Paco Nathan, Director of Community Evangelism at Databricks
Apache Spark is intended as a fast and powerful general purpose engine for processing Hadoop data. Spark supports combinations of batch processing, streaming, SQL, ML, Graph, etc., for applications written in Scala, Java, Python, Clojure, and R, among others. In this talk, I'll explore how Spark fits into the Big Data landscape. In addition, I'll describe other systems with which Spark pairs nicely, and will also explain why Spark is needed for the work ahead.
What’s new in Spark 2.0?
Rerngvit Yanggratoke @ Combient AB
Örjan Lundberg @ Combient AB
Machine Learning Stockholm Meetup
27 October, 2016
Schibsted Media Group
when new subject come into existence ,we have to give a place among already existing subject. this ppt will help to how can we assign a place to particular subject.it will helpful for all the students whom are pursuing their master in library science ans information management
Introduction to MARC
History (MARC to MARC 21)
Why MARC 21/Need of MARC 21
Characteristics
Design principle for MARC 21
MARC 21 Documentation
MARC 21Record System
MARC 21 Communication formats
MARC 21 Format for Bibliographic Data
Component of bibliographic record
Communication Standard
Mapping of MARC 21
MARC 21 Translation
Maintenance Agency
MARC 21 Regulation
Advantage of MARC 21
Problems with MARC 21
Future of MARC 21
A comparative analysis of library classification systemsAli Hassan Maken
We use classification each & every moment of the life by intentionally or unintentionally. Classification has always been the backbone of all Library operations and without it, library is definitely going to suffer in its recourse and to find a particular piece of information from unorganized heap of knowledge is almost impossible. The library classification is core instrument for organizing and retrieval of the documents stored in a library. At present era they are the navigation tools for locating and retrieving documents in more precisely and relevantly. The electronic versions of the DDC and UDC and other classification schemes make it possible to realize the potential of library classification to improve subject retrieval.
presentation on "CATALOGUING" during Training workshop in library science for staff of muktangan school libraries organised by muktangan school teacher reference library, mumbai on 15th November 2010
A presentation on Digital Library Software by Rupesh Kumar A, Assistant Professor, Department of Studies and Research in Library and Information Science, Tumkur University, Tumakuru, Karnataka, India.
Anglo-American Cataloguing Rules AACR2 to acquire an international adaptability.Cataloging & Classification.AACR1 and AACR2.AACR1 Anglo-American Cataloging Rules. North American text. Chicago: American Library Association, 1967.
AACR1, Chap. 12 Anglo-American Cataloging Rules. North American text. Chapter 12. Chicago: American Library
Association,
1975.
AACR2 Anglo-American Cataloguing Rules. 2nd ed. Chicago: American Library Association, 1
This PPT contain details of Z39.50 and useful for Library Science students. This protocol used for information retrieval and in the end list of different types of protocols are given.
Collection Development (that based on the five laws of S.R.Ranghanathan) is very important part of Collection Management. If we don’t adopt advanced technologies, collection then we can never fulfill the need of advanced users and libraries will become freeze, this is against the 5th law of Ranghanathan that “ Library is a growing organism”.
Big Data Everywhere Chicago: Apache Spark Plus Many Other Frameworks -- How S...BigDataEverywhere
Paco Nathan, Director of Community Evangelism at Databricks
Apache Spark is intended as a fast and powerful general purpose engine for processing Hadoop data. Spark supports combinations of batch processing, streaming, SQL, ML, Graph, etc., for applications written in Scala, Java, Python, Clojure, and R, among others. In this talk, I'll explore how Spark fits into the Big Data landscape. In addition, I'll describe other systems with which Spark pairs nicely, and will also explain why Spark is needed for the work ahead.
What’s new in Spark 2.0?
Rerngvit Yanggratoke @ Combient AB
Örjan Lundberg @ Combient AB
Machine Learning Stockholm Meetup
27 October, 2016
Schibsted Media Group
Lotusphere 2007 AD507 Leveraging the Power of Object Oriented Programming in ...Bill Buchan
Co-presented with Jens Augustini
Object Oriented Programming (OOP) may drastically reduce your coding time in projects that reach a higher degree of complexity, as it brings re-usable and consistent logic in the form of your own objects to your fingertips. This session will show how to create and use your own classes and how they can relate to the LotusScript Object Model. If you are familiar with LotusScript but don't know how to create your own classes, this session is for you!
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
---
Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
Strata NYC 2015 - What's coming for the Spark communityDatabricks
In the last year Spark has seen substantial growth in adoption as well as the pace and scope of development. This talk will look forward and discuss both technical initiatives and the evolution of the Spark community.
On the technical side, I’ll discuss two key initiatives ahead for Spark. The first is a tighter integration of Spark’s libraries through shared primitives such as the data frame API. The second is across-the-board performance optimizations that exploit schema information embedded in Spark’s newer APIs. These initiatives are both designed to make Spark applications easier to write and faster to run.
On the community side, this talk will focus on the growing ecosystem of extensions, tools, and integrations evolving around Spark. I’ll survey popular language bindings, data sources, notebooks, visualization libraries, statistics libraries, and other community projects. Extensions will be a major point of growth in the future, and this talk will discuss how we can position the upstream project to help encourage and foster this growth.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Assignment # 2PreliminariesImportant Points· Evidence of acad.docxjane3dyson92312
Assignment # 2Preliminaries
Important Points
· Evidence of academic misconduct (e.g., plagiarism, collaboration/collusion among students) will be taken seriously and University regulations strictly followed.
· You are expected to produce a word-processed answer to this assignment. Please use Arial font and a font size of 12. For SQL code and output, you can use courier new, which preserves SQL format and layout.
· You are required to use the Harvard Style of referencing and citation. The “Cite them right” guide is recommended for referencing and citation (Pears and Shields, 2008) which should be followed throughout your answer especially Part 3.
· Late submissions will be given zero marks unless prior permission is gained from the school office/programme leader.
Module Learning Outcomes (MLOs) assessed:
Knowledge & Understanding:
2. Key concepts of data warehousing.
Intellectual / Professional skills & abilities:
3. Conceptual data modelling, relational database design and implementation in SQL & PL/SQL, and object-based databases.
4. Design and Implementation of a data warehouse using Oracle database system.
Tasks of the Assignment
Part 1 (50 marks) Scenario: Mechanical Production Factories (MPF) Database System
MPF is a company that produces customised mechanical products within Europe. The company produces a rang of mechanical products at several factories. Information about which work force are assigned to which production orders and kept in the force usage register.
In order to access information quickly and to ensure that all past records are available for audit purposes, the company developed a database. Figure 1 shows a UML class diagram, which provides a conceptual model of the database. Relational Design for MPF Database System
A conceptual model of a database may be implemented using any database system (e.g. relational, object-relational, object-oriented). However, to start with, we have mapped the MPF’s conceptual model onto a relational logical model. Figure 2 details the relations for an implementation of the database using a relational database system. Note that Figure 2 uses shorthand / abbreviated notation for data types / domains for describing various attributes of the relations involved in the database.
Figure 1: UML Class Diagram for the MPF Database
Domains/Data Types: ID = Number(6) LTXT = Varchar(50)
STXT = Varchar(30) DEC = Number(8, 2) INT = Number(6)
Factory (FactoryId: ID, Location: LTXT, Country: LTXT)
Product (ProdId: ID, Description: LTXT, CostPerItem: DEC, LabCostPerItem: DEC)
FactoryProduct (FactoryId: ID *: ID, ProdId*: ID)
Workforce (wfId: ID, wfName: STXT, yearlyIncome: DEC, yearlyTax: DEC, taxCode: INT, factoryId*: ID)
Production (prodOrderId: ID, quantity: INT, itemPrice: DEC,orderDate: Date, promiseDate: Date, completionDate: Date, shipmentDate: Date, status: CHAR, prodID*: ID)
ForceUsage (wfId*:ID, prodOrderId*:I.
OAP: Optimized Analytics Package for Spark Platform with Daoyuan Wang and Yua...Databricks
Spark SQL is one of the most popular components in big data warehouse for SQL queries in batch mode, and it allows user to process data from various data sources in a highly efficient way. However, Spark SQL is a general purpose SQL engine and not well designed for ad hoc queries. Intel invented an Apache Spark data source plugin called Spinach for fulfilling such requirements, by leveraging user-customized indices and fine-grained data cache mechanisms.
To be more specific, Spinach defines a new Parquet-like data storage format, offering a fine-grained hierarchical cache mechanism in the unit of “Fiber” in memory. Even existing Parquet or ORC data files can be loaded using corresponding adaptors. Data can be cached in off-heap memory to boost data loading. What’s more, Spinach has extended the Spark SQL DDL, to allow users to define the customized indices based on relation. Currently, B+ tree and bloom filter are the first two types of indices supported. Last but not least, since Spinach resides in the process of Spark executor, there’s no extra effort in deployment. All you need to do is to pick Spinach from Spark packages when launching the Spark SQL.
sing corresponding adaptors. Data can be cached in off-heap memory to boost data loading. What’s more, Spinach has extended the Spark SQL DDL, to allow user to define the customized indices based on relation. Currently B+ tree and bloom filter are the first 2 types of index we’ve supported. Last but not least, since Spinach resides in the process of Spark executor, there’s no extra effort in deployment, all we need to do is to pick Spinach from Spark packages when launch the Spark SQL.
Spinach has been imported in Baidu’s production environment since Q4 2016. It helps several teams migrating their regular data analysis tasks from Hive or MR jobs to ad-hoc queries. In Baidu search ads system FengChao, data engineers analyze advertising effectiveness based on several TBs data of display and click logs every day. Spinach brings a 5x boost compared to original Spark SQL (version 2.1), especially in the scenario of complex search and large data volume. It optimizes the average search cost from minutes to seconds, while brings only 3% data size increase for adding a single index.
Your data is getting bigger while your boss is getting anxious to have insights! This tutorial covers Apache Spark that makes data analytics fast to write and fast to run. Tackle big datasets quickly through a simple API in Python, and learn one programming paradigm in order to deploy interactive, batch, and streaming applications while connecting to data sources incl. HDFS, Hive, JSON, and S3.
A full Machine learning pipeline in Scikit-learn vs in scala-Spark: pros and ...Jose Quesada (hiring)
The machine learning libraries in Apache Spark are an impressive piece of software engineering, and are maturing rapidly. What advantages does Spark.ml offer over scikit-learn? At Data Science Retreat we've taken a real-world dataset and worked through the stages of building a predictive model -- exploration, data cleaning, feature engineering, and model fitting; which would you use in production?
The machine learning libraries in Apache Spark are an impressive piece of software engineering, and are maturing rapidly. What advantages does Spark.ml offer over scikit-learn?
At Data Science Retreat we've taken a real-world dataset and worked through the stages of building a predictive model -- exploration, data cleaning, feature engineering, and model fitting -- in several different frameworks. We'll show what it's like to work with native Spark.ml, and compare it to scikit-learn along several dimensions: ease of use, productivity, feature set, and performance.
In some ways Spark.ml is still rather immature, but it also conveys new superpowers to those who know how to use it.
Composable Parallel Processing in Apache Spark and WeldDatabricks
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Speaker: Matei Zaharia
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A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
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- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
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- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
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However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
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- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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https://alandix.com/academic/papers/synergy2024-epistemic/
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Speakers:
Bob Boule
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Monitoring Java Application Security with JDK Tools and JFR Events
Libsys 7 to koha
1. Libsys 7 to Koha
Data Migration , Customization & Implementation
of Open Source ILMS
CHAITANYA PRATAP
SINGH
MASTER OF COMPUTER
APPLICATIONS,
SOUTH ASIAN UNIVERSITY, NEW
DELHI
2. ABOUT THE ORGANIZATION
National Institute of Science Communication and
Information Resources (NISCAIR), located at New
Delhi, India, is one of the premier information science
institutes in India under the umbrella of CSIR (Council of
Scientific and Industrial Research) that comprise 38 other
labs/institutes of different disciplines spread across the
country.
NISCAIR came into existence on 30 September 2002 with
the merger of National Institute of Science
Communication (NISCOM) and Indian National
Scientific Documentation Centre (INSDOC). Both
NISCOM and INSDOC, the two premier institutes of the
Council of Scientific and Industrial Research (CSIR), were
devoted to dissemination and documentation of S&T
information, respectively.
4. OBJECTIVE OF CSIR-CAT
To Implement Open Source Integrated Library System
software Koha at the place of proprietary software
which are already in use.
To do federated search in Koha for distributed libraries
i.e. 39 National Libraries of India.
5. NEED OF KOHA
The open source software solutions are very cost effective as
compared to proprietary software solutions this initiative will
also boost-up the movement of open source in India and thus
the millions of Rupees can be saved on software.
Libsys- Rs. 4,50,000
They charge Rs. 10,000 on each arrival.
You have to purchase different modules otherwise it will not
work.
7. WHY CSIR-CAT?
1. Reduction of cost of ownership of ILMS
2. Networking of CSIR KRCs
3. Implementation of uniform & international standard across all CSIR
KRCs like MARC 21, Z39.50 so that data can be migrated to any
other format
4. Avoid vendor locking for ILMS by using open source software
5. Increase in efficiency for Information Scientists due to reduced
classification efforts of knowledge managers/ information scientists
by importing/sharing catalogues from each other or from other
online sources like library of congress
6. Optimum utilization/sharing of information resources through
ILL available in KRCs
7. Avoid duplication of resources like books, monographs, reports,
thesis, standards, patents, etc among CSIR KRC
12. HOW DATA HAS BEEN MIGRATED
FROM LIBSYS7 TO KOHA?
1. Generated multiple text report files with different filelds.
Accession number is printed in all the files to join them
latter on. It took three days because connection was too slow
2. Converted these text files to excel
3. Removed headers, footers and blank rows through macro
4. Converted these files in RDBMS tables
14. DATA COLLECTION MODEL
1. Bibliographic Record
2. Authority Record
3. Patron Record
4. Serial Record
5. Acquisition Record
6. Circulation Record
15. PROBLEM AND SOLUTION WHILE
FETCHING THE DATA OF LIBSYS 7.0
As the existing software does not provide import/export
feature, but it does provide report generation to a file.
During this process we took the output as a text file covering
all the fields in the catalogue such as,
Title;
authors;
edition;
place of publication;
publisher name;
year;
pagination;
ISBN;
class number;
book number;
accession number;
16. DATA CLEANING
Generated multiple reports with different
columns,
Trimmed the extra space between the words,
Deleted all blank lines
Wrote a program to bring multi-line text to single
line,
If title was distributed in 3 lines then we
converted that multi-line to single line
17. FORMATTING OF THE TEXT FILE
Figure 1. Original text file (accession number, title and author
field).
18. Figure 2: Original text file (accession number, edition, publisher
location,
publisher name and year of publishing).
19. PROBLEM IN TEXT FILE
N-Number of blank lines are present, data shown is having slash to
distinguish author and title but in some records we don’t have slash also
23. SOLUTION :
In order to solve this we wrote macro’s for
1. Deleting blank lines
2. Deleting page numbers
3. Bringing the record in a Single Line with
correct Access number
24. MACRO FOR DELETING BLANK
ROWS
Sub DeleteBlankRows()
' This macro deletes all rows on the active worksheet
' that have no value in column D.
Dim iRow As Long
Dim LastRow As Long
LastRow = ActiveSheet.UsedRange.Rows.Count +
ActiveSheet.UsedRange.Row - 1
For iRow = LastRow To 1 Step -1
If Cells(iRow, 1) = "" And Cells(iRow, 2) = "" And Cells(iRow, 3) = "" And
Cells(iRow, 4) = "" And Cells(iRow, 5) = "" Then Rows(iRow).Delete
If Cells(iRow, 1) <> "" And Cells(iRow, 2) = "" And Cells(iRow, 3) = "" And
Cells(iRow, 4) = "" And Cells(iRow, 5) = "" Then Rows(iRow).Delete
Next iRow
End Sub
25. MERGING INTO EXCEL
Figure 3: Multiple spreadsheets merged to form a single spreadsheet
(control number, accession number/barcode, title, author, isbn,
publishing location, class number, publisher name and
pagination).
27. You will be prompted for mapping the fields to
recognize the fields by standard marc format.
Suppose for Field 0 that is first column I
entered Map to: 008 (control number) and then
click on Apply.
31. MRK STRUCTURE FOR SINGLE
RECORD
=LDR 00421nam a2200193Ia 45e0
=001 1
=003 CSIR-NISTADS
=008
130228s9999xx0000undd
=040 $aCSIR-NISTADS$cCSIR-NISTADS
=100 $aBrown, Michael Barratt
=245 $aEconomics of Imperialism
=260 $aLondon
=260 $bPenguin
=300 $a380
=500 $a1
=850 $aCSIR-NISTADS
=902 $a335.412, BRO
=942 $cBK
=952 $p1
32. CONVERT .MRK FILE TO .MRC
Convert .mrk file into raw Marc format that can
be directly imported into Koha.
For this again open MarcEdit and Select MARC
Tools.
Next Select MarcMaker to convert .mrk file
into .mrc format.
Locate your input file and name your output file.
Then Click Execute.
35. ABOUT KOHA
Koha is an integrated library system (ILS)
It was the first open source ILS.
Koha was created in 1999 by Katipo Communications for
the Horowhenua Library Trust in New Zealand.
The first installation went live in January of 2000.
45. OPAC CONFIGURATION
Decide how you want your OPAC to look and
what content you want on the main page.
Create a library branded stylesheet using CSS.
Create a custom XSLT stylesheet to change the
way search results and bibliographic records
appear in the OPAC.
Define OPAC system preferences.
Set up your cron jobs.
48. PROBLEM WITH Z39.50
1. Implementation not easy
2. Does not scale well (if nodes > 100)
3. Network bandwidth
4. Z39.50 implementation at client
(“Origin’) end
5. Time Consuming
6. Slow Processing
7. All Servers should be on.
53. CONCLUSION & FUTURE
WORK
Data from proprietary software migrated to Koha.
Barcode Scanner will be attached.
Now Union catalogue for distributed libraries will be
made by using oai-pmh protocol and harvester for
federated searching.
Using harvester, data can be converted back to marc
for implementing Z39.50 protocol in koha for
importing records from distributed libraries.