Sub themes ELAG 2017Posted: November 9, 2016
ELAG 2017 – Automation
For many years, library automation was focused on increasing access to (meta)data collections and improving end user interfaces. These fields are still important and of interest (we welcome workshop proposals and interesting presentation proposals). This year however we would like to focus on library workflows. What are the current tools available to full automate typical back office activities. How can ‘intelligent’ devices provide the best services possible? Can in our age of self-driving cars, intelligent personal assistants, and artificial intelligence, library software be upgraded into fully automated machines? What metrics can be used to measure the success of these services? What kind of automation hardware and software need to enter the library IT- stack?
I hope you are just excited about this theme as we are.
Workflow automation is the class of procedures that manage and orchestrate business activities, resources and services in an organization. Workflow engines provide the tools to create a full description of all the required steps in business activities and monitors the state of these activities. Workflows can be tuned and optimized (semi)-automatically to maximize specific goals (e.g. shortest time to serve a request, maximize processed items). Examples of workflows in libraries are the steps required for moving items to and from the depots (from user request, to printing of a depot slip, to fetching books, to make them available to the end users while keeping track of the status), or a scanning digitization workflows (fetching books, scanning, post-processing, metadata management, digital archiving and providing access copies), or even managing servers (use microservices to automatically increase processing power when needed).
ELAG is interested not only in the workflow engines themselves but especially how automation is used to build intelligent systems which provide an optimized execution of typical library services.
We would like you to talk about:
- How to create intelligent (automated) workflows to do activities one couldn’t do before?
- Automation of existing workflows, where can machines take over and lead an activity?
- How is orchestration used in your library to automatically maximize services?
- Which metrics and statistics are being used to measure and optimize the service success rate?
- What tools are used to describe and orchestrate library workflows?
Visualization links the two most powerful information processing systems known: the human mind and the modern computer. As a process, it transforms data, information and knowledge into a visual form exploiting people’s natural strengths in rapid visual pattern recognition. Effective visual interfaces enable us to observe, manipulate, search, navigate, explore, filter, discover, understand, and interact with large volume of data far more rapidly and far more effectively to discover hidden patterns. The impact of visualization has been fundamental leading to new insights and more efficient decision-making.
Reporting is a fundamental part of business intelligence and knowledge management. These implementations involve extract, transform, and load (ETL) procedures in coordination with a data warehouse and using reporting tools.
One of the best ways to get a message across is to use visualization to quickly draw attention to the key messages, and by presenting data visually it’s also possible to uncover surprising patterns and observations that wouldn’t be apparent from looking at statistics alone. Reporting and visualization can be used to present the library’s achievements for example when creating monthly or annuals reports and making them more interesting and easier to read.
For many years librarians have developed best practices to assist them in the selection and acquisition of materials they believed best to fit their patrons. Recently libraries had to adjust to the shift from print to electronic. Libraries have already developed bibliographic structures to accommodate the printed book and its acquisition, description and classification. Today through demand driven actions users become partners in collection development. We should check the implementation of DDA (Demand Driven Actions) in library collection, the costs for the libraries, as well as collection sustainability.
Some things to consider for automatic response of systems on registered metrics:
- Data that help inform collections management and strategy
- Data to assist in the development of new services and to improve the user experience
- Data to better understand user behaviors
- Data that uncover new types of impact and value for institutions and organizations
Libraries are offering a wide range of self services for their patrons: self registration of library accounts, self checkout and return of physical items, payment machines for library fees, access to learning and reading rooms, download of electronic resources, or even 24/7 complete self service libraries like the “Open Libraries” in Denmark. When these services are user friendly, safe and robust, they can help to cut costs and improve user satisfaction in libraries. They must integrate well with the wider library automation environment, like integrated library systems, patron directories, payment systems etc.
We also see new self-services being offered by libraries, like ISBN or DOI self registration services.
We would like to hear about:
- What services are you planning to offer as new self-services in the future? And how?
- What are the technical problems implementing self-services? And how to solve them?
- What library services are hard to offer as a self-service? And how did you manage to do it anyway?
- What about “self services” like Sci-Hub?
Libraries are there to help users find the information they need. New waves of information overload bring new kinds of information technology into play. Does the current information deluge require the use of machine learning to cope? Are you providing automated reading processes to aid your users navigate the flow of new articles in their fields? Do you operate an infrastructure for extracting and recombining data entities from scientific literature? Is automated pattern recognition an integral part of your subject indexing process? Or do you employ Named-entity Recognition, or Natural Language Processing, in a smart way? How much AI goes into your discovery and recommendation services? Maybe you don’t have full-fledged programs in production – but perhaps you have run a pilot? Tell us how it worked out (or worked not). Or if you’re planning a project, make your case and lay out a framework for engaged reactions and advice.
We would like to hear about:
- Machine learning
- Automated reading
- Automated abstracting and indexing
- Use of predictive algorithms.
- Use of automized semantic science entities
- Linked science automation
- Recommending systems
Robots, hardware, Raspberry Pi, Arduino
The Internet of things (IoT) can be divided in two broad categories: 1) information and analysis, 2) Automation and control.
In information and analytics category, there are the types of applications:
- Tracking behavior
- Enhanced situational awareness
- Sensor-driven decision analytics
For automation and control, we can describe the following types of applications:
- Process optimization
- Optimized resource consumption
- Complex autonomous systems
Some of the potential areas for implementation of IoT in libraries include the following:
- RFID (Libraries already use RFID technology for self checkout, but it can be used for information sharing also)
- popular aisles (pressure pads under the floor can collect data about people standing, so we can extract statistics about how popular some study areas are)
- library wearable’s (for example, library card or a wrist band could hold all the person’s information, give access to computers, reading history etc)
- beacons (they can be used to locate people in the library and give personalized info based on person’s interests and their current location)
- mobile payments, ticketing and event registration (these devices will be able to tell users about overdue books and how much fine they owe to the library, to enable them return the overdue books and pay the fine online without needing to stand in a queue in the library)
We would like to hear you talk about:
- Which hardware tools are entering your library?
- In what way these devices can work autonomous and make “intelligent” decisions?
- How do these machines change the library environment and what new tasks are possible that required a lot of manual labor in the past?