38 research outputs found
Service Systems, Smart Service Systems and Cyber- Physical Systems—What’s the difference? Towards a Unified Terminology
As businesses and their networks transform towards co-creation, several concepts describing the resulting systems emerge. During the past years, we can observe a rise of the concepts Service Systems, Smart Service Systems and Cyber-Physical Systems. However, distinct definitions are either very broad or contradict each other. As a result, several characteristics appear around these terms, which also miss distinct allocations and relationships to the underlying concepts. Previous research only describes these concepts and related characteristics in an isolated manner. Thus, we perform an inter-disciplinary structured literature review to relate and define the concepts of Service Systems, Smart Service Systems and Cyber-Physical Systems as well as related characteristics. This article can, therefore, serve as a basis for future research endeavors as it delivers a unified terminology
An end-to-end process model for supervised machine learning classification: from problem to deployment in information systems
Extracting meaningful knowledge from (big) data represents a key success factor in many industries today. Supervised machine learning (SML) has emerged as a popular technique to learn patterns in complex data sets and to identify hidden correlations. When this insight is turned into action, business value is created. However, common data mining processes are generally not tailored to SML. In addition, they fall short of providing an end-to-end view that not only supports building a ”one off” model, but also covers its operational deployment within an information system. In this research-in-progress work we apply a Design Science Research (DSR) approach to develop a SML process model artifact that comprises model initiation, error estimation and deployment. In a first cycle, we evaluate the artifact in an illustrative scenario to demonstrate suitability. The results encourage us to further refine the approach and to prepare evaluations in concrete use cases. Thus, we move towards contributing a general process model that supports the systematic design of machine learning solutions to turn insights into continuous action
Service-Oriented Cognitive Analytics for Smart Service Systems: A Research Agenda
The development of analytical solutions for smart services systems relies on data. Typically, this data is distributed across various entities of the system. Cognitive learning allows to find patterns and to make predictions across these distributed data sources, yet its potential is not fully explored. Challenges that impede a cross-entity data analysis concern organizational challenges (e.g., confidentiality), algorithmic challenges (e.g., robustness) as well as technical challenges (e.g., data processing). So far, there is no comprehensive approach to build cognitive analytics solutions, if data is distributed across different entities of a smart service system. This work proposes a research agenda for the development of a service-oriented cognitive analytics framework. The analytics framework uses a centralized cognitive aggregation model to combine predictions being made by each entity of the service system. Based on this research agenda, we plan to develop and evaluate the cognitive analytics framework in future research
Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning
Successful analytics solutions that provide valuable insights often hinge on
the connection of various data sources. While it is often feasible to generate
larger data pools within organizations, the application of analytics within
(inter-organizational) business networks is still severely constrained. As data
is distributed across several legal units, potentially even across countries,
the fear of disclosing sensitive information as well as the sheer volume of the
data that would need to be exchanged are key inhibitors for the creation of
effective system-wide solutions -- all while still reaching superior prediction
performance. In this work, we propose a meta machine learning method that deals
with these obstacles to enable comprehensive analyses within a business
network. We follow a design science research approach and evaluate our method
with respect to feasibility and performance in an industrial use case. First,
we show that it is feasible to perform network-wide analyses that preserve data
confidentiality as well as limit data transfer volume. Second, we demonstrate
that our method outperforms a conventional isolated analysis and even gets
close to a (hypothetical) scenario where all data could be shared within the
network. Thus, we provide a fundamental contribution for making business
networks more effective, as we remove a key obstacle to tap the huge potential
of learning from data that is scattered throughout the network.Comment: Preprint, forthcoming at Information Technology and Managemen
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability
In networks of independent entities that face similar predictive tasks, transfer machine learning enables to re-use and improve neural nets using distributed data sets without the exposure of raw data. As the number of data sets in business networks grows and not every neural net transfer is successful, indicators are needed for its impact on the target performance-its transferability. We perform an empirical study on a unique real-world use case comprised of sales data from six different restaurants. We train and transfer neural nets across these restaurant sales data and measure their transferability. Moreover, we calculate potential indicators for transferability based on divergences of data, data projections and a novel metric for neural net similarity. We obtain significant negative correlations between the transferability and the tested indicators. Our findings allow to choose the transfer path based on these indicators, which improves model performance whilst simultaneously requiring fewer model transfers
How to Conduct Rigorous Supervised Machine Learning in Information Systems Research: The Supervised Machine Learning Reportcard [in press]
Within the last decade, the application of supervised machine learning (SML) has become increasingly popular in the field of information systems (IS) research. Although the choices among different data preprocessing techniques, as well as different algorithms and their individual implementations, are fundamental building blocks of SML results, their documentation—and therefore reproducibility—is inconsistent across published IS research papers.
This may be quite understandable, since the goals and motivations for SML applications vary and since the field has been rapidly evolving within IS. For the IS research community, however, this poses a big challenge, because even with full access to the data neither a complete evaluation of the SML approaches nor a replication of the research results is possible.
Therefore, this article aims to provide the IS community with guidelines for comprehensively and rigorously conducting, as well as documenting, SML research: First, we review the literature concerning steps and SML process frameworks to extract relevant problem characteristics and relevant choices to be made in the application of SML. Second, we integrate these into a comprehensive “Supervised Machine Learning Reportcard (SMLR)” as an artifact to be used in future SML endeavors. Third, we apply this reportcard to a set of 121 relevant articles published in renowned IS outlets between 2010 and 2018 and demonstrate how and where the documentation of current IS research articles can be improved. Thus, this work should contribute to a more complete and rigorous application and documentation of SML approaches, thereby enabling a deeper evaluation and reproducibility / replication of results in IS research
Tensor Decomposition Reveals Concurrent Evolutionary Convergences and Divergences and Correlations with Structural Motifs in Ribosomal RNA
Evolutionary relationships among organisms are commonly described by using a
hierarchy derived from comparisons of ribosomal RNA (rRNA) sequences. We propose that
even on the level of a single rRNA molecule, an organism's evolution is composed
of multiple pathways due to concurrent forces that act independently upon different
rRNA degrees of freedom. Relationships among organisms are then compositions of
coexisting pathway-dependent similarities and dissimilarities, which cannot be
described by a single hierarchy. We computationally test this hypothesis in
comparative analyses of 16S and 23S rRNA sequence alignments by using a tensor
decomposition, i.e., a framework for modeling composite data. Each alignment is
encoded in a cuboid, i.e., a third-order tensor, where nucleotides, positions and
organisms, each represent a degree of freedom. A tensor mode-1 higher-order singular
value decomposition (HOSVD) is formulated such that it separates each cuboid into
combinations of patterns of nucleotide frequency variation across organisms and
positions, i.e., “eigenpositions” and corresponding nucleotide-specific
segments of “eigenorganisms,” respectively, independent of a-priori
knowledge of the taxonomic groups or rRNA structures. We find, in support of our
hypothesis that, first, the significant eigenpositions reveal multiple similarities
and dissimilarities among the taxonomic groups. Second, the corresponding
eigenorganisms identify insertions or deletions of nucleotides exclusively conserved
within the corresponding groups, that map out entire substructures and are enriched
in adenosines, unpaired in the rRNA secondary structure, that participate in tertiary
structure interactions. This demonstrates that structural motifs involved in rRNA
folding and function are evolutionary degrees of freedom. Third, two previously
unknown coexisting subgenic relationships between Microsporidia and Archaea are
revealed in both the 16S and 23S rRNA alignments, a convergence and a divergence,
conferred by insertions and deletions of these motifs, which cannot be described by a
single hierarchy. This shows that mode-1 HOSVD modeling of rRNA alignments might be
used to computationally predict evolutionary mechanisms