Chat reference is becoming prevalent in academic libraries across the country. This paper details the experience of East Tennessee State University's Sherrod Library when initiating such a service at their institution during the 2001-2002 academic year, which began as a response to the addition of a new online degree program in the state. Software selection, staff training, management issues, and statistical data are presented. Data analyzed includes use patterns, types of questions, and categories of users. The paper details librarians' reactions to providing reference services in this format, including the problems encountered. It also offers suggestions for a more successful implementation.

Chat reference is becoming prevalent in academic libraries across the country. This paper details the experience of East Tennessee State University’s Sherrod Library when initiating such a service at their institution during the 2001-2002 academic year, which began as a response to the addition of a new online degree program in the state. Software selection, staff training, management issues, and statistical data are presented. Data analyzed includes use patterns, types of questions, and categories of users. The paper details librarians’ reactions to providing reference services in this format, including the problems encountered. It also offers suggestions for a more successful implementation.

Chat reference is becoming prevalent in academic libraries across the country. This paper details the experience of East Tennessee State University’s Sherrod Library when initiating such a service at their institution during the 2001-2002 academic year, which began as a response to the addition of a new online degree program in the state. Software selection, staff training, management issues, and statistical data are presented. Data analyzed includes use patterns, types of questions, and categories of users. The paper details librarians’ reactions to providing reference services in this format, including the problems encountered. It also offers suggestions for a more successful implementation.

As the development of hardware progresses, computers are expected to solve increasingly complex problems. However, solving more complex problems requires more complex software. To be able to develop these software systems, new programming languages with new features and higher abstraction levels are introduced. These features are designed to ease development, but sometimes they also make the runtime behavior unpredictable. Such features can not be used in real-time systems. A feature that traditionally has been unpredictable is garbage collection. Moreover, even though a garbage collector frees unused memory, almost all such methods require large amounts of additional memory. Garbage collection relieves developers of the responsibility to reclaim memory that is no longer used by the application. This is very tedious and error prone if done manually. Since garbage collection increases productivity and decreases programming errors, developers find it attractive, also in the real-time domain. This thesis presents a predictable garbage collection method, real-time reference counting, that increases memory efficiency by about 50 % compared to the most memory efficient previously presented predictable garbage collector. To increase performance, an optimization technique called object ownership that eliminates redundant reference count updates is presented. Object ownership is designed for reference counters, but can also be used to increase the performance of other incremental garbage collectors. Finally, a static garbage collector is presented. The static garbage collector can allocate objects statically or on the runtime stack, and insert explicit instructions to reclaim memory allocated on the heap. It makes it possible to eliminate the need for runtime garbage collection for a large class of Java applications. The static garbage collection method can also be used to remove costly synchronization instructions. Competing static garbage collection methods with reasonable analysis time are restricted to stack allocation, and thus handle a smaller class of applications.