TY - JOUR A1 - Oltmanns, Elias A1 - Hasler, Tim A1 - Peters-Kottig, Wolfgang A1 - Kuper, Heinz-Günter T1 - Different Preservation Levels: The Case of Scholarly Digital Editions JF - Data Science Journal N2 - Ensuring the long-term availability of research data forms an integral part of data management services. Where OAIS compliant digital preservation has been established in recent years, in almost all cases the services aim at the preservation of file-based objects. In the Digital Humanities, research data is often represented in highly structured aggregations, such as Scholarly Digital Editions. Naturally, scholars would like their editions to remain functionally complete as long as possible. Besides standard components like webservers, the presentation typically relies on project specific code interacting with client software like webbrowsers. Especially the latter being subject to rapid change over time invariably makes such environments awkward to maintain once funding has ended. Pragmatic approaches have to be found in order to balance the curation effort and the maintainability of access to research data over time. A sketch of four potential service levels aiming at the long-term availability of research data in the humanities is outlined: (1) Continuous Maintenance, (2) Application Conservation, (3) Application Data Preservation, and (4) Bitstream Preservation. The first being too costly and the last hardly satisfactory in general, we suggest that the implementation of services by an infrastructure provider should concentrate on service levels 2 and 3. We explain their strengths and limitations considering the example of two Scholarly Digital Editions. KW - Digital Preservation Y1 - 2019 U6 - https://doi.org/10.5334/dsj-2019-051 VL - 18 IS - 1(51) ER - TY - GEN A1 - Witzig, Jakob A1 - Berthold, Timo T1 - Conflict-Free Learning for Mixed Integer Programming N2 - Conflict learning plays an important role in solving mixed integer programs (MIPs) and is implemented in most major MIP solvers. A major step for MIP conflict learning is to aggregate the LP relaxation of an infeasible subproblem to a single globally valid constraint, the dual proof, that proves infeasibility within the local bounds. Among others, one way of learning is to add these constraints to the problem formulation for the remainder of the search. We suggest to not restrict this procedure to infeasible subproblems, but to also use global proof constraints from subproblems that are not (yet) infeasible, but can be expected to be pruned soon. As a special case, we also consider learning from integer feasible LP solutions. First experiments of this conflict-free learning strategy show promising results on the MIPLIB2017 benchmark set. T3 - ZIB-Report - 19-59 KW - mixed integer programming KW - conflict analysis KW - dual proof analysis KW - no-good learning KW - solution learning Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-75338 SN - 1438-0064 ER -