Auflistung nach Autor:in "Oberhofer, Martin"
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- KonferenzbeitragEmbedded analytics in front office applications(Datenbanksysteme in Business, Technologie und Web (BTW) – 13. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2009) Nijkamp, Erik; Oberhofer, MartinToday, decision making by users of front office applications happens without analytical information supporting this process. We propose as solution Embedded Analytics (EA) making analytical insight in context of the front office application available to u
- KonferenzbeitragInteractive predictive analytics with columnar databases(Datenbanksysteme für Business, Technologie und Web (BTW), 2011) Oberhofer, Martin; Wurst, MichaelPredictive Analytics is usually seen as highly interactive task. Paradoxically, it is still performed mostly as a batch task. This does not only limit its applicability, it also sets it apart from a task that is conceptually very close to it, namely OLAP analysis. The main reason for considering mining a batch task is the usually very high execution time on large data warehouses. While novel hardware offers the ability of highly distributed execution of predictive analytics algorithms, this level of parallelism cannot be exploited within the traditional row-based database paradigm. Columnar databases offer a solution to this problem, as the underlying datastructures lend themselves very well to parallel execution. This reduces the repsonse time for mining queries several magnitudes for some algorithms. While making mining faster and more responsive is already nice in itself, the real value of low response times is allowing completely new ways of interacting with huge data warehouses. In this arcticle we give a survey on the opportunities and challanges of interative, OLAP-like mining and on how columnar databases can support it. We exemplify these ideas on a task that is especially attractive for interactive mining, namely outlier detection in large data warehouses.
- TextdokumentMachine Learning Applied to the Clerical Task Management Problem in Master Data Management Systems(BTW 2019, 2019) Oberhofer, Martin; Bremer, Lars; Chkalova, MariyaClerical tasks are created if a duplicate detection algorithm detects some similarity of records but not enough to allow an auto-merge operation. Data stewards review clerical tasks and make a final non-match or match decision. In this paper we evaluate different machine learning algorithms regarding their accuracy to predict the correct action for a clerical task and execute that action automatically if the prediction has sufficient confidence. This approach reduces the amount of work for data stewards by factors of magnitude.
- KonferenzbeitragMetadata-driven data migration for SAP projects(Datenbanksysteme für Business, Technologie und Web (BTW), 2011) Oberhofer, Martin; Maier, Albert; Schwarz, Thomas; Vodegel, ManfredSAP applications are mission-critical for many enterprises today. However, projects to introduce a new SAP solution or consolidate existing SAP solutions often fail respectively overrun budget and time. A common root cause is the underestimation of data migration work. Data quality in legacy systems is often not sufficient for SAP, and specifications of the target data model often change very late in the project lifecycle, e.g. due to new business requirements or new insights about legacy systems and legacy business processes. This can cause significant re-work in the ETL jobs that extract data from source systems, cleanse that data and load it into the target SAP system(s). We apply a model-driven architecture (MDA) approach [MP10] to such data migration projects. We generate ETL infrastructure from SAP metadata. This novel approach (known as the IBM Ready-To-Launch (RTL) for SAP solution [Ibm10]) significantly reduces project risk and cost. In addition, data quality is addressed and improved. Our demo will show programmatic access to SAP metadata and its systematic exploitation throughout the data migration project, including the generation of logical and physical data models from this metadata, and the generation of ETL jobs.
- KonferenzbeitragSupport 2.0: An optimized product support system exploiting master data, data warehousing and web 2.0 technologies(Datenbanksysteme in Business, Technologie und Web (BTW) – 13. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2009) Oberhofer, Martin; Maier, AlbertThe proposed system integrates traditional and Web 2.0 based product support systems and uses master data management, data warehousing and text analytics functionality to send problem records to the person most capable to solve it, let it be an in-house t
- KonferenzbeitragValue demonstration of embedded analytics for front office applications(Datenbanksysteme in Business, Technologie und Web (BTW) – 13. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2009) Nijkamp, Erik; Oberhofer, Martin; Maier, AlbertUsers of front office applications such as call center or customer support applications make millions and millions of decisions each day without analytical support. For example, if a support employee gets a new support ticket and needs to decide how much