Data Mining for Service Planning and Management in Libraries
Published: October 3, 2019 by Dr. Geoffrey Liu
With operation being increasingly computerized and services becoming interactive online, libraries – like other business organizations – are accumulating huge piles of data. Such data include not only operational/circulation records and online transactions on web platform, but also textual information generated by library virtual communities and data collected through service programs. In this sense, the “Big Data” movement did not leave libraries out.
However, in spite of much talking about “big data” in libraries and the call for “mining library data” appearing in 1990s (Banerjee, 1998; Mancini, 1996; Peters, 1996) and growing stronger later (Guenther, 2000; Collier, 2003; Dhiman, 2003; Papatheodorou, Kapidakis, Sfakakis, & Vassiliou, 2003; Prakash, Chand, & Gohel, 2004; Lavoie, Dempsey, & Connaway, 2006; Chiang, 2010; Shieh, 2010; etc.), serious projects of mining library data are scarce, and most published efforts were limited to mining web transaction logs (Banks, 2000; Blecic et al., 1998; Jones, Cunningham, McNab, & Boddie, 2000; Peters, 1996; Yan, Zhang, Tang, Sun, Deng, & Xiao, 2010; Warren, 2002;). Only a few researchers went beyond mining transaction logs, and the work by Wu’s team (Wu, 2003; Wu, Lee, & Kao, 2004) and Nicholson’s (2003a; 2003b; 2006) focus on “biblioming” are particularly worthy to note here.
The possibilities of applying data mining in library settings and information services are abundant, given the richness of data accumulated therein. Besides mining web transaction logs for understanding usage trends and circulation transactions for investigating readership trends (Yu, 2011), service/program-specific data (such as records of online reference services) may be mined for managerial decision making, strategic planning, resource allocation, and optimization of operational processes (e.g. Kao, Chang, & Lin, 2003). Furthermore, data models of prediction can be trained with records of technical processing to develop computer-aided workflows for improved effectiveness and efficiency, as demonstrated by Wagstaff and Liu’s (2018) work on improving the collection-weeding process.
But all of the possibilities hinge on libraries’ willingness to invest in in-house data mining or to collaborate and share their data with researchers.
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