Hasso-Plattner-Institut
HPI Digital Health Center
  
 

Digital Health Center

2021

  • Ganzinger, M., Glaab, E., Kerssemakers, J., Nahnsen, S., Sax, U., Schaadt, N.S., Schapranow, M.-P., Tiede, T.: Biomedical and Clinical Research Data Management. In: Wolkenhauer, O. (ed.) Systems Medicine. pp. 532 - 543. Academic Press, Oxford (2021).
     

2020

  • Schapranow, M.-P.: Good News: Wie Data Science dabei hilft, die Corona-Pandemie besser zu verstehen.Portal Wissen: Das Forschungsmagazin der Universität Potsdam.9,14--19 (2020).
     
  • Konigorski, S., Monti, R., Rautenstrauch, P., Lippert, C.: Fast kernel-based rare-variant association tests integrating variant annotations from deep learning.Genetic Epidemiology. p. 495 (2020).
     
  • da Cruz, H.F., Pfahringer, B., Martensen, T., Schneider, F., Meyer, A., Bottinger, E., Schapranow, M.-P.: Using Interpretability Approaches to Update textquotedblleftBlack-Boxtextquotedblright Clinical Prediction Models: an External Validation Study in Nephrology.Artificial Intelligence in Medicine.101982 (2020).
     
  • Somani, S.S., Richter, and F., Fuster, V., Freitas, J.K.D., Naik, N., Sigel, K., Bottinger, E.P., Levin, M.A., Fayad, Z., Just, A.C., Charney, A.W., Zhao, S., Glicksberg, B.S., Lala, A., Nadkarni, G.N.: Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization for COVID-19.Journal of General Internal Medicine.35,2838--2844 (2020).
     
  • Schapranow, M.-P.: #nCoVStats: Wie Data Science hilft die Coronavirus-Pandemie zu verstehen.gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland.13,34--37 (2020).
     
  • Vaid, A., Somani, S., Russak, A.J., Freitas, J.K.D., Chaudhry, F.F., Paranjpe, I., Johnson, K.W., Lee, S.J., Miotto, R., Richter, F., Zhao, S., Beckmann, N.D., Naik, N., Kia, A., Timsina, P., Lala, A., Paranjpe, M., Golden, E., Danieletto, M., Singh, M., Meyer, D., OtextquotesingleReilly, P.F., Huckins, L., Kovatch, P., Finkelstein, J., Freeman, R.M., Argulian, E., Kasarskis, A., Percha, B., Aberg, J.A., Bagiella, E., Horowitz, C.R., Murphy, B., Nestler, E.J., Schadt, E.E., Cho, J.H., Cordon-Cardo, C., Fuster, V., Charney, D.S., Reich, D.L., Bottinger, E.P., Levin, M.A., Narula, J., Fayad, Z.A., Just, A.C., Charney, A.W., Nadkarni, G.N., Glicksberg, B.S.: Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.Journal of Medical Internet Research.22,e24018 (2020).
     
  • Chan, L., Jaladanki, S.K., Somani, S., Paranjpe, I., Kumar, A., Zhao, S., Kaufman, L., Leisman, S., Sharma, S., He, J.C., Murphy, B., Fayad, Z.A., Levin, M.A., Bottinger, E.P., Charney, A.W., Glicksberg, B.S., Coca, S.G., Nadkarni, G.N.: Outcomes of Patients on Maintenance Dialysis Hospitalized with COVID-19.Clinical Journal of the American Society of Nephrology.CJN.12360720 (2020).
     
  • Fehr, J., Konigorski, S., Lippert, C.: Data Science für Digitale Medizin. Presented at the (2020).
     
  • Rüdiger, S., Konigorski, S., Edelman, J., Zernick, D., Lippert, C., Thieme, A.: The SARS-CoV-2 effective reproduction rate has a high correlation with a contact index derived from large-scale individual location data using GPS-enabled mobile phones in Germany, https://doi.org/10.1101/2020.10.02.20188136, (2020).
     
  • Borchert, F., Lohr, C., Modersohn, L., Langer, T., Follmann, M., Sachs, J.P., Hahn, U., Schapranow, M.-P.: GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines.Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis. p. 38--48. Association for Computational Linguistics, Online (2020).
     
  • Stegbauer, J.S., Kraus, M., Nordmeyer, S., Kirchner, M., Ziehm, M.Z., Dommisch, H., Kelle, S., Kelm, M., Baczko, I., Landmesser, U., Tschope, C., Knosalla, C., Falcke, M., Schapranow, M.-P., Regitz-Zagrosek, V., Mertins, P., Kühne, T.: Proteomic analysis reveals upregulation of ACE2, the putative SARS-CoV-2 receptor in pressure- but not volume-overloaded human hearts.Hypertension. (2020).
     
  • Piccininni, M., Konigorski, S., Rohmann, J.L., Kurth, T.: Directed Acyclic Graphs and causal thinking in clinical risk prediction modeling.BMC Medical Research Methodology.20,179 (2020).
     
  • Galka, A., Moontaha, S., SIniatchkin, S.: Constrained expectation maximisation algorithm for estimating ARMA models in state space representation.EURASIP Journal on Advances in Signal Processing 2020.1.1-37 (2020).
     
  • Sigel, K., Swartz, T., Golden, E., Paranjpe, I., Somani, S., Richter, F., Freitas, J.K.D., Miotto, R., Zhao, S., Polak, P., Mutetwa, T., Factor, S., Mehandru, S., Mullen, M., Cossarini, F., Bottinger, E., Fayad, Z., Merad, M., Gnjatic, S., Aberg, J., Charney, A., Nadkarni, G., Glicksberg, B.S.: Coronavirus 2019 and People Living With Human Immunodeficiency Virus: Outcomes for Hospitalized Patients in New York City.Clinical Infectious Diseases. (2020).
     
  • Chaudhary, K., Vaid, A., Duffy, Á., Paranjpe, I., Jaladanki, S., Paranjpe, M., Johnson, K., Gokhale, A., Pattharanitima, P., Chauhan, K., O'Hagan, R., Vleck, T.V., Coca, S.G., Cooper, R., Glicksberg, B., Bottinger, E.P., Chan, L., Nadkarni, G.N.: Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.Clinical Journal of the American Society of Nephrology.CJN.09330819 (2020).
     
  • Van Hout, C.V., Tachmazidou, I., Backman, J.D., Hoffman, J.D., Liu, D., Pandey, A.K., Gonzaga-Jauregui, C., Khalid, S., Ye, B., Banerjee, N., Li, A.H., O'Dushlaine, C., Marcketta, A., Staples, J., Schurmann, C., Hawes, A., Maxwell, E., Barnard, L., Lopez, A., Penn, J., Habegger, L., Blumenfeld, A.L., Bai, X., O'Keeffe, S., Yadav, A., Praveen, K., Jones, M., Salerno, W.J., Chung, W.K., Surakka, I., Willer, C.J., Hveem, K., Leader, J.B., Carey, D.J., Ledbetter, D.H., Cardon, L., Yancopoulos, G.D., Economides, A., Coppola, G., Shuldiner, A.R., Balasubramanian, S., Cantor, M., Nelson, M.R., Whittaker, J., Reid, J.G., Marchini, J., Overton, J.D., Scott, R.A., Abecasis, G.R., Yerges-Armstrong, L., Baras, A.: Exome sequencing and characterization of 49,960 individuals in the UK Biobank.Nature. (2020).
     
  • Moontaha, S., Steckhan, N., Kappattanavar, A., Surges, R., Arnrich, B.: Self-prediction of seizures in drug resistance epilepsy using digital phenotyping: a concept study.Pervasive Health. (2020).
     
  • Wilkinson, J., Arnold, K.F., Murray, E.J., van Smeden, M., Carr, K., Sippy, R., de Kamps, M., Beam, A., Konigorski, S., Lippert, C., Gilthorpe, M.S., Tennant, P.W.G.: It is time to reality check the promises of machine learning-powered precision medicine.The Lancet Digital Health. (2020).
     
  • Meier, I., Schablon, A., Nienhaus, A., Konigorski, S.: Latente Tuberkulose bei medizinischem Personal in Deutschland nach Auslandseinsatz.Pneumologie.74,1-7 (2020).
     
  • Kappattanavar, A.M., da Cruz, H.F., Arnrich, B., Böttinger, E.: Position Matters: Sensor Placement for SittingPosture Classification.IEEE International Conference on Healthcare Informatics. (2020).
     
  • Zhou, L., Fischer, E., Tunca, C., Brahms, C.M., Ersoy, C., Granacher, U., Arnrich, B.: How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications.Sensors. (2020).
     
  • Albert, J., Owolabi, V., Gebel, A., Brahms, C.M., Granacher, U., Arnrich, B.: Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study.Sensors. (2020).
     
  • Sasso, A.M., Datta, S., Jeitler, M., Steckhan, N., Kessler, C.S., Michalsen, A., Arnrich, B., Boettinger, E.: HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population.International Conference on Artificial Intelligence in Medicine. p. 325--335. Springer (2020).
     
  • Yaghootkar, H., Zhang, Y., Spracklen, C.N., Karaderi, T., Huang, L.O., Bradfield, J., Schurmann, C., Fine, R.S., Preuss, M.H., Kutalik, Z., Wittemans, L.B., Lu, Y., Metz, S., Willems, S.M., Li-Gao, R., Grarup, N., Wang, S., Molnos, S., Sandoval-Zárate, A.A., Nalls, M.A., Lange, L.A., Haesser, J., Guo, X., Lyytikäinen, L.-P., Feitosa, M.F., Sitlani, C.M., Venturini, C., Mahajan, A., Kacprowski, T., Wang, C.A., Chasman, D.I., Amin, N., Broer, L., Robertson, N., Young, K.L., Allison, M., Auer, P.L., Blüher, M., Borja, J.B., Bork-Jensen, J., Carrasquilla, G.D., Christofidou, P., Demirkan, A., Doege, C.A., Garcia, M.E., Graff, M., Guo, K., Hakonarson, H., Hong, J., Ida Chen, Y.-D., Jackson, R., Jakupović, H., Jousilahti, P., Justice, A.E., Kähönen, M., Kizer, J.R., Kriebel, J., LeDuc, C.A., Li, J., Lind, L., Luan, J.'an, Mackey, D., Mangino, M., Männistö, S., Martin Carli, J.F., Medina-Gomez, C., Mook-Kanamori, D.O., Morris, A.P., de Mutsert, R., Nauck, M., Nedeljkovic, I., Pennell, C.E., Pradhan, A.D., Psaty, B.M., Raitakari, O.T., Scott, R.A., Skaaby, T., Strauch, K., Taylor, K.D., Teumer, A., Uitterlinden, A.G., Wu, Y., Yao, J., Walker, M., North, K.E., Kovacs, P., Ikram, M.A., van Duijn, C.M., Ridker, P.M., Lye, S., Homuth, G., Ingelsson, E., Spector, T.D., McKnight, B., Province, M.A., Lehtimäki, T., Adair, L.S., Rotter, J.I., Reiner, A.P., Wilson, J.G., Harris, T.B., Ripatti, S., Grallert, H., Meigs, J.B., Salomaa, V., Hansen, T., Willems van Dijk, K., Wareham, N.J., Grant, S.F., Langenberg, C., Frayling, T.M., Lindgren, C.M., Mohlke, K.L., Leibel, R.L., Loos, R.J., Kilpeläinen, T.O.: Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity.Diabetes. (2020).
     
  • Chan, L., Chaudhary, K., Saha, A., Chauhan, K., Vaid, A., Zhao, S., Paranjpe, I., Somani, S., Richter, F., Miotto, R., Lala, A., Kia, A., Timsina, P., Li, L., Freeman, R., Chen, R., Narula, J., Just, A.C., Horowitz, C., Fayad, Z., Cordon-Cardo, C., Schadt, E., Levin, M.A., Reich, D.L., Fuster, V., Murphy, B., He, J.C., Charney, A.W., Böttinger, E.P., Glicksberg, B.S., Coca, S.G., Nadkarni, G.N., Li, L.: AKI in Hospitalized Patients with COVID-19.Journal of the American Society of Nephrology.ASN.2020050615 (2020).
     
  • Remy, S., Pufahl, L., Sachs, J.P., Böttinger, E., Weske, M.: Event Log Generation in a Health System: A Case Study.Lecture Notes in Computer Science. p. 505--522. Springer International Publishing (2020).
     
  • Pfitzner, B., Steckhan, N., Arnrich, B.: Federated Learning in a Medical Context: A Systematic Literature Review.ACM Transactions on Internet Technology (TOIT) Special Issue on Security and Privacy of Medical Data for Smart Healthcare. (2020).
     
  • Schmid, R., Pfitzner, B., Beilharz, J., Arnrich, B., Polze, A.: Tangle Ledger for Decentralized Learning.2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 852-859 (2020).
     
  • Zhou, L., Tunca, C., Fischer, E., Brahms, C.M., Ersoy, C., Granacher, U., Arnrich, B.: Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations.42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2020).
     
  • Kraus, M., Mathew Stephen, M., Schapranow, M.-P.: Eatomics: Shiny exploration of quantitative proteomics data.Journal of Proteome Research. (2020).
     
  • Sasso, A.M., Datta, S., Jeitler, M., Steckhan, N., Kessler, C.S., Michalsen, A., Arnrich, B., Boettinger, E.: HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population.International Conference on Artificial Intelligence in Medicine. p. 325--335. Springer (2020).
     
  • Trilla, I., Drimalla, H., Bajbouj, M., Dziobek, I.: The Influence of Reward on Facial Mimicry: No Evidence for a Significant Effect of Oxytocin.Frontiers in Behavioural Neuroscience. (2020).
     
  • Drimalla, H., Scheffer, T., Landwehr, N., Baskow, I., Roepke, S., Behnia, B., Dziobek, I.: Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT).npj digital medicine.3, (2020).
     
  • Paranjpe, I., Chaudhary, K., Paranjpe, M., O'Hagan, R., Manna, S., Jaladanki, S., Kapoor, A., Horowitz, C., DeFelice, N., Cooper, R., Glicksberg, B., Bottinger, E.P., Just, A.C., Nadkarni, G.N.: Association of APOL1 Risk Genotype and Air Pollution for Kidney Disease.Clinical Journal of the American Society of Nephrology.15,401--403 (2020).
     
  • Paranjpe, I., Russak, A., Freitas, J.K.D., Lala, A., Miotto, R., Vaid, A., Johnson, K.W., Danieletto, M., Golden, E., Meyer, D., Singh, M., Somani, S., Manna, S., Nangia, U., Kapoor, A., OtextquotesingleHagan, R., OtextquotesingleReilly, P.F., Huckins, L.M., Glowe, P., Kia, A., Timsina, P., Freeman, R.M., Levin, M.A., Jhang, J., Firpo, A., Kovatch, P., Finkelstein, J., Aberg, J.A., Bagiella, E., Horowitz, C.R., Murphy, B., Fayad, Z.A., Narula, J., Nestler, E.J., Fuster, V., Cordon-Cardo, C., Charney, D.S., Reich, D.L., Just, A.C., Bottinger, E.P., Charney, A.W., Glicksberg, B.S., Nadkarni, G.: Clinical Characteristics of Hospitalized Covid-19 Patients in New York City.medRxiv.Version 1 (April 23, 2020 - 03:18) (2020).
     
  • Slosarek, T., Wohlbrandt, A., Böttinger, E.: Using CEF Digital Service Infrastructures in the Smart4Health Project for the Exchange of Electronic Health Records.arXiv preprint arXiv:2001.01477. (2020).
     
  • Schapranow, M.-P.: Hand in Hand: Wie KI und Ärzte in der Onkologie zusammenarbeiten.Konkrete Anwendungsfälle von KI & Big-Data in der Industrie.69--74 (2020).
     
  • Konigorski, S., Yilmaz, Y.E., Janke, J., Bergmann, M.M., Boeing, H., Pischon, T.: Powerful rare variant association testing in a copula-based joint analysis of multiple traits.Genetic Epidemiology.44,26-40 (2020).
     
  • Borchert, F., Lohr, C., Modersohn, L., Hahn, U., Langer, T., Wenzel, G., Follmann, M., Schapranow, M.-P.: "Herr Doktor, verstehen Sie mich?“: Wie lernende Systeme helfen medizinische Fachsprache zu verstehen und welche Rolle klinische Leitlinien dabei spielen.gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland.13,19--22 (2020).
     
  • Lewkowicz, D., Wohlbrandt, A., Boettinger, E.: Economic impact of clinical decision support interventions based on electronic health records.BMC Health Services Research.20, (2020).
     

2019

  • Konak, O., Freitas Da Cruz, H., Thiele, M., Golla, D., Schapranow, M.-P.: An Information and Communication Platform Supporting Analytics for Elderly Care.5th International Conference on Information for Ageing Well, Communication Technologies e Health (2019).
     
  • Drimalla, H., Landwehr, N., Hess, U., Dziobek, I.: From face to face: the contribution of facial mimicry to cognitive and emotional empathy.Cognition and Emotion.33,1672-1686 (2019).
     
  • Konigorski, S., Monti, R., Lippert, C.: Kernel-based tests integrating variant effect predictions from deep learning for genetic association tests of rare variants.64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS) (2019).
     
  • Konigorski, S., Janke, J., Drogan, D., Bergmann, M.M., Hierholzer, J., Kaaks, R., Boeing, H., Pischon, T.: Prediction of circulating adipokine levels based on body fat compartments and adipose tissue gene expression.Obesity Facts.12,590-605 (2019).
     
  • Schapranow, M.-P.: Für bessere Diagnosen und Therapien: Wie Ärzte und KI in der Krebsbehandlung zusammenarbeiten, https://www.wissenschaftsjahr.de/2019/neues-aus-der-wissenschaft/das-sagt-die-wissenschaft/wie-kuenstliche-intelligenz-das-gesundheitswesen-nachhaltig-unterstuetzt/, (2019).
     
  • Schapranow, M.-P., others, and: A Federated In-memory Database System for Life Sciences. In: Castellanos, M., Chrysanthis, P., and Pelechrinis, K. (eds.) Real-Time Business Intelligence and Analytics. BIRTE 2015, BIRTE 2016, BIRTE 2017. Springer, Cham (2019).
     
  • Morassi Sasso, A., Datta, S., Pfitzner, B., Zhou, L., Steckhan, N., Boettinger, E., Arnrich, B.: Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions.Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters. EAI, Trento (2019).
     
  • Wojcik, G.L., Graff, M., Nishimura, K.K., Tao, R., Haessler, J., Gignoux, C.R., Highland, H.M., Patel, Y.M., Sorokin, E.P., Avery, C.L., Belbin, G.M., Bien, S.A., Cheng, I., Cullina, S., Hodonsky, C.J., Hu, Y., Huckins, L.M., Jeff, J., Justice, A.E., Kocarnik, J.M., Lim, U., Lin, B.M., Lu, Y., Nelson, S.C., Park, S.-S.L., Poisner, H., Preuss, M.H., Richard, M.A., Schurmann, C., Setiawan, V.W., Sockell, A., Vahi, K., Verbanck, M., Vishnu, A., Walker, R.W., Young, K.L., Zubair, N., Acuna-Alonso, V., Ambite, J.L., Barnes, K.C., Boerwinkle, E., Bottinger, E.P., Bustamante, C.D., Caberto, C., Canizales-Quinteros, S., Conomos, M.P., Deelman, E., Do, R., Doheny, K., Fernandez-Rhodes, L., Fornage, M., Hailu, B., Heiss, G., Henn, B.M., Hindorff, L.A., Jackson, R.D., Laurie, C.A., Laurie, C.C., Li, Y., Lin, D.-Y., Moreno-Estrada, A., Nadkarni, G., Norman, P.J., Pooler, L.C., Reiner, A.P., Romm, J., Sabatti, C., Sandoval, K., Sheng, X., Stahl, E.A., Stram, D.O., Thornton, T.A., Wassel, C.L., Wilkens, L.R., Winkler, C.A., Yoneyama, S., Buyske, S., Haiman, C.A., Kooperberg, C., Le Marchand, L., Loos, R.J.F., Matise, T.C., North, K.E., Peters, U., Kenny, E.E., Carlson, C.S.: Genetic analyses of diverse populations improves discovery for complex traits.Nature.570,514--518 (2019).
     
  • Belbin, G.M., Wenric, S., Cullina, S., Glicksberg, B.S., Moscati, A., Wojcik, G.L., Shemirani, R., Beckmann, N.D., Cohain, A., Sorokin, E.P., Park, D.S., Ambite, J.-L., Ellis, S., Auton, A., Bottinger, E.P., Cho, J.H., Loos, R.J.F., Abul husn, N.S., Zaitlen, N.A., Gignoux, C.R., Kenny, E.E., and,: Towards a fine-scale population health monitoring system. (2019).
     
  • Freitas da Cruz, H., Pfahringer, B., Schneider, F., Meyer, A., Schapranow, M.-P.: External Validation of a “Black-Box” Clinical Predictive Model in Nephrology: Can Interpretability Methods Help Illuminate Performance Differences?Proceedings of 17th Conference on Artificial Intelligence in Medicine. pp. 191-201 (2019).
     
  • Freitas da Cruz, H., Schneider, F., Schapranow, M.-P.: Prediction of Acute Kidney Injury in Cardiac Surgery Patients: Interpretation using Local Interpretable Model-agnostic Explanations.Proceedings of the 12th International Conference on Biomedical Engineering Systems and Technologies. pp. 380-387. , Prague, Czech Republic (2019).
     
  • Romo Ventura, E., Konigorski, S., Rohrmann, S., Schneider, H., Stalla, G.K., Pischon, T., Linseisen, J., Nimptsch, K.: Association of dietary intake of milk and dairy products with blood concentrations of insulin-like growth factor 1 (IGF-1) in Bavarian adults.European Journal of Nutrition.59,1413–1420 (2019).
     
  • Morassi Sasso, A., Datta, S., Pfitzner, B., Zhou, L., Steckhan, N., Boettinger, E., Arnrich, B.: Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions.Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters. EAI, Trento (2019).
     
  • Muñoz-González, L., Pfitzner, B., Russo, M., Carnerero-Cano, J., Lupu, E.C.: Poisoning Attacks with Generative Adversarial Nets.arXiv:1906.07773. (2019).
     
  • Freitas da Cruz, H., Horschig, S., Nusshag, C., Schapranow, M.-P.: Knowledge Distillation from Machine Learning Models for Prediction of Hemodialysis Outcomes.International Journal On Advances in Life Sciences.11,33-43 (2019).
     
  • Datta, S., Schraplau, A., da Cruz, H.F., Sachs, J.P., Mayer, F., Böttinger, E.: A Machine Learning Approach for Non-Invasive Diagnosis of Metabolic Syndrome.2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). p. 933--940. IEEE (2019).
     
  • Hernández, N., Arnrich, B., Favela, J., Ersoy, C., Demiray, B., Fontecha, J.: A multi-site study on walkability, data sharing and privacy perception using mobile sensing data gathered from the mk-sense platform.Journal of Ambient Intelligence and Humanized Computing.10,2199-2211 (2019).
     
  • Moontaha, S., Galka, A., Siniatchkin, M., Scharlach, S., von Spiczak, S., Stephani, U., May, T., Meurer, T.: SVD Square-root Iterated Extended Kalman Filter for Modeling of Epileptic Seizure Count Time Series with External Inputs.41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. (2019).
     
  • Steckhan, N., Arnrich, B.: Digital Transformation of Complementary and Alternative Medicine (Accepted Presubmission).Complementary Medicine Research. (2019).
     
  • Drimalla, H., Baskow, I., Roepke, S., Behnia, B., Dziobek, I.: Imitation und Erkennung von Emotionen bei Autismus-Spektrum-Störungen - eine computerbasierte Analyse des fazialen Emotionsausdrucks.12. Wissenschaftliche Tagung Autismus-Spektrum. (2019).
     
  • Avery, E.G., Balogh, A., Bartolomaeus, H., Löber, U., Steckhan, N., Markó, L., Wilck, N., Hamad, I., Šušnjar, U., Mähler, A., Hohmann, C., Lesker, T.R., Strowig, T., Dechend, R., Bzdok, D., Kleinewietfeld, M., Andreas, M., Müller, D.N., Forslund, S.K.: Integrative Network Analysis Of Microbiome-Immune Axis In Metabolic Syndrome Patients During A Fasting Intervention.Hypertension.74, (2019).
     
  • Morassi Sasso, A., Datta, S., Pfitzner, B., Zhou, L., Steckhan, N., Boettinger, E., Arnrich, B.: Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions.Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters. EAI, Trento (2019).
     
  • Yekta Said, C., Arnrich, B., Ersoy, C.: Stress detection in daily life scenarios using smart phones and wearable sensors: A survey.Journal of Biomedical Informatics.92,103139 (2019).
     
  • Nimptsch, K., Konigorski, S., Pischon, T.: Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine.Metabolism.92,61--70 (2019).
     
  • Netzahualcoyotl, H., Demiray, B., Arnrich, B., Favela, J.: An Exploratory Study to Detect Temporal Orientation Using Bluetooth's sensor.13th EAI International Conference on Pervasive Computing Technologies for Healthcare.292-297 (2019).
     
  • Slosarek, T., Kraus, M., Schapranow, M.-P., Bottinger, E.: Qualitative Comparison of Selected Indel Detection Methods for RNA-Seq Data.International Work-Conference on Bioinformatics and Biomedical Engineering. pp. 166-177. Springer (2019).
     
  • Scharlach, S., Moontaha, S., von Spiczak, S., Stephani, U., Siniatchkin, M., May, T., Galka, A., Meurer, T.: Bewertung von Therapieeffekten bei Epilepsie: Eine vergleichende Analyse zwischen Cox-Stuart-Berechnung und Zustandsraum-Modellierung.11. Gemeinsame Jahrestagung der Deutschen und Österreichischen Gesellschaft für Epileptologie sowie der Schweizerischen Epilepsie-Liga. (2019).
     
  • Freitas da Cruz, H., Bergner, B., Konak, O., Schneider, F., Bode, P., Lempert, C., Schapranow, M.-P.: MORPHER – A Platform to Support Modeling of Outcome and Risk Prediction in Health Research.Proceedings of the 19th IEEE International Conference on Bioinformatics and Biomedicine. , Athens, Greece (2019).
     

2018

  • Hesse, G., Reissaus, B., Matthies, C., Lorenz, M., Kraus, M., Uflacker, M.: Senska - Towards an Enterprise Streaming Benchmark.Performance Evaluation and Benchmarking for the Analytics Era: 9th TPC Technology Conference, TPCTC 2017, Munich, Germany, August 28, 2017, Revised Selected Papers. pp. 25-40. Springer International Publishing (2018).
     
  • Freitas da Cruz, H., Horschig, S., Nusshag, C., Schapranow, M.-P.: Prediction of Patient Outcomes after Renal Replacement Therapy in Intensive Care.Proceedings of the 3rd International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing (2018).
     
  • Freitas da Cruz, H., Gebhardt, M., Becher, F., Schapranow, M.-P.: Interactive Data Exploration Supporting Elderly Care Planning.Proceedings of the 10th International Conference on eHealth, Telemedicine, and Social Medicine (2018).
     
  • Konigorski, S., Khorasani, S., Lippert, C.: Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer's disease.Machine Learning for Health (ML4H) Workshop at NeurIPS 2018, arXiv:1812.00448 (2018).
     
  • Oleynik, M., Faessler, E., Sasso, A.M., Kappattanavar, A., Bergner, B., da Cruz, H.F., Sachs, J.-P., Datta, S., Boettinger, E.: HPI-DHC at TREC 2018 Precision Medicine Track.Notebook papers of the TREC 2018 conference. pp. 1-9 (2018).
     
  • Konigorski, S., Lippert, C.: Kernel-based tests for very rare variants.Genetic Epidemiology. p. 711 (2018).
     
  • Oleynik, M., Faessler, E., Sasso, A.M., Kappattanavar, A., Bergner, B., da Cruz, H.F., Sachs, J.-P., Datta, S., Boettinger, E.: HPI-DHC at TREC 2018 Precision Medicine Track. (2018).
     
  • Moontaha, S., Galka, A., Meurer, T., Siniatchkin, M.: Analysis of the effects of medication for the treatment of epilepsy by ensemble Iterative Extended Kalman Filtering.40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. (2018).
     
  • Kraus, M., Hesse, G., Slosarek, T., Danner, M., Kesar, A., Bhushan, A., Schapranow, M.-P.: DEAME-Differential Expression Analysis Made Easy.Heterogeneous Data Management, Polystores, and Analytics for Healthcare. p. 162--174. Springer (2018).
     

2017

  • Hildenbrand, D., Albert, J., Charrier, P., Steinmetz, C.: Geometric Algebra Computing for Heterogeneous Systems.Advances in Applied Clifford Algebras.27,599-620 (2017).
     
  • Yoshiura, V.T., Azevedo-Marques, J.~ao M., Rzewuska, M., Vinci, A.L.T., Sasso, A.M., Miyoshi, N.S.B.~ao, Furegato, A.R.F., Rijo, R.P.C.L., Del-Ben, C.M., Alves, D.: A web-based information system for a regional public mental healthcare service network in Brazil.International journal of mental health systems.11,1 (2017).
     
  • Schaar, K., Geisler, A., Kraus, M., Pinkert, S., Pryshliak, M., Spencer, J.F., Tollefson, A.E., Ying, B., Kurreck, J., Wold, W.S., others,: Anti-adenoviral artificial MicroRNAs expressed from AAV9 vectors inhibit human adenovirus infection in immunosuppressed Syrian hamsters.Molecular Therapy-Nucleic Acids.8,300--316 (2017).
     
  • Moontaha, S., von Spiczak, S., Scharlach, S., Doege, C., Boor, R., May, T., Stephani, U., Siniatchkin, M., Galka, A.: Evaluation der Therapieeffekte antikonvulsiver Medikamente bei Kindern mit strukturell bedingter Epilepsie mittels Zustandsraum-Modellierung.43. Jahrestagung der Gesellschaft für Neuropädiatrie.1-45 (2017).
     
  • Kraus, M., Schapranow, M.-P.: An In-Memory Database Platform for Systems Medicine.Proceedings of the International Conference on Bioinformatics and Computational Biology. p. 93--100. The International Society for Computers and Their Applications (ISCA) (2017).
     
  • Schapranow, M.-P., Brauer, J., Plattner, H.: The Data Donation Pass: Enabling Sovereign Control of Personal Healthcare Data.Proceedings of the World Congress in Computer Science, Computer Engineering, and Applied Computing. CSCE (2017).
     
  • Neves, M., Folkerts, H., Jankrift, M., Niedermeier, J., Stachewicz, T., Tietböhl, S., Kraus, M., Uflacker, M.: Olelo: A Question Answering Application for Biomedicine.ACL'17 Demo (2017).
     
  • Kraus, M., Niedermeier, J., Jankrift, M., Tietboehl, S., Stachewicz, T., Folkerts, H., Uflacker, M., Neves, M.: Olelo: a web application for intuitive exploration of biomedical literature.Nucleic acids research. (2017).
     
  • Schapranow, M.-P.: Die digitale Transformation mitgestalten — Der Datenspendeausweis: Souveräner Umgang mit persönlichen Gesundheitsdaten.Plattform Life Sciences.38--39 (2017).
     

2016

  • Schapranow, M.-P., Uflacker, M., Sariyar, M., Semler, S., Fichte, J., Schielke, D., Ekinci, K., Zahn, T.: Towards An Integrated Health Research Process: A Cloud-based Approach.Proceedings of The IEEE International Conference on Big Data.2813--2818 (2016).
     
  • Postel, M.: Geographical Exploration of Key Performance Indicators for Elderly Care Planning, (2016).
     
  • Horschig, F.: Prediction of Health Research Data using In-Memory Database Technology, (2016).
     
  • Schapranow, M.-P.: Datenspendeausweis für ­Bürger: Ein Plädoyer für mündige Patienten, die die eigenen Gesundheitsdaten am besten verstehen.Management & Krankenhaus. (2016).
     
  • Schapranow, M.-P., Kraus, M., Danner, M., Plattner, H.: IMDBfs: Bridging the Gap between In-Memory Database Technology and File-Based Tools for Life Sciences.Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine. p. 1133--1139. IEEE (2016).
     
  • Freitas da Cruz, H., Grasnick, B., Dinger, H., Bier, F., Meinel, C.: Early Detection of Acute Kidney Injury with Bayesian Networks.Proceedings of the 7th International Symposium on Semantic Mining in Biomedicine. pp. 29-36 (2016).
     
  • Rückert, L.: Real-time Exploration of Healthcare Data using In-Memory Database Technology, (2016).
     
  • Cruz, H., Grasnick, S., Dinger, H.: ADRiAS: Acute Disease Risk Assessment System. (2016).
     
  • Neves, M., Kraus, M.: BioMedLAT Corpus: Annotation of the Lexical Answer Type for Biomedical Questions.OKBQA 2016. p. 49 (2016).
     
  • Schapranow, M.-P.: Die In-Memory-Technologie in der personalisierten Medizin, https://news.sap.com/germany/die-in-memory-technologie-in-der-personalisierten-medizin/, (2016).
     

2015

  • Dobkwicz, M., Jüttner, T., Freitas da Cruz, H., Heidtke, K., Finkenwirth, T., Kunze, S., Hänold, S., Nwankwo, I., Forgó, N., Schröder, C., Graf, N.: Implementation and Demonstration of the p-BioSPRE Metabiobank Platform.Demo at the 15th World Congress on Health and Biomedical Informatics (2015).
     
  • Denecke, K., Mall, S., Fähnrich, C., Perscheid, C., Adeoye, O.O., Benzler, J., Claus, H., Kirchner, G., Richter, R., Schapranow, M.-P., Schwarz, N., Reigl, L., Tom-Aba, D., Gidado, S., Waziri, N.E., Uflacker, M., Krause, G., Poggensee, G.: „Surveillance and Outbreak Response Management and Analysis System (SORMAS)“ ermöglicht Kontrolle von Ebola-Infizierten in Westafrika.10. Jahrestagung der Deutschen Gesellschaft für Epidemiologie (DGEpi) (2015).
     
  • Schapranow, M.-P., Perscheid, C., Wachsmann, A., Siegert, M., Bock, C., Horschig, F., Liedke, F., Brauer, J., Plattner, H.: A Federated In-Memory Database System For Life Sciences.Proceedings of the 9th International Workshop on Business Intelligence for the Real Time Enterprise (BIRTE) (2015).
     
  • Massicano, F., Sasso, A., Tomaz, H., Oleynik, M., Nobrega, C., Patrao, D.F.C.: An Ontology for TNM Clinical Stage Inference.ONTOBRAS (2015).
     
  • Patr~ao, D.F.C., Oleynik, M., Massicano, F., Sasso, A.M.: Recruit-An Ontology Based Information Retrieval System for Clinical Trials Recruitment.MedInfo. p. 534--538 (2015).
     
  • Fähnrich, C., Schapranow, M.-P., Plattner, H.: Facing the Genome Data Deluge: Efficiently Identifying Genetic Variants with In-Memory Database Technology.Proceedings of the ACM Symposium on Applied Computing (2015).
     
  • Schapranow, M.-P., Kraus, M., Perscheid, C., Bock, C., Liedtke, F., Plattner, H.: The Medical Knowledge Cockpit: Real-time Analysis of Big Medical Data Enabling Precision Medicine.Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM). pp. 770-775 (2015).
     
  • Fähnrich, C., Denecke, K., Adeoye, O., Benzler, J., Claus, H., Kirchner, G., Mall, S., Richter, R., Schapranow, M.-P., Schwarz, N.G., Tom-Aba, D., Uflacker, M., Poggensee, G., Krause, G.: Surveillance and Outbreak Response Management System (SORMAS) to support the control of the Ebola virus disease outbreak in West Africa.Euro Surveillance. (2015).
     
  • Schapranow, M.-P., Perscheid, C., Plattner, H.: IT-Aided Business Process Enabling Real-time Analysis of Candidates for Clinical Trials.Proceedings of the 4th International Conference on Global Health Challenges. pp. 67-73. IARIA (2015).
     

2014

  • Haeger, F., Schapranow, M.-P., Fähnrich, C., Ziegler, E., Plattner, H.: In-Memory Computing Enabling Real-time Genome Data Analysis.International Journal on Advances in Life Sciences, Vol 6, Nr 1-2. (2014).
     
  • Herbst, K., Fähnrich, C., Neves, M., Schapranow, M.-P.: Applying In-Memory Technology for Automatic Template Filling in the Clinical Domain.CLEF 2014 Evaluation Labs and Workshop, Online Working Notes (2014).
     
  • Fähnrich, C., Schapranow, M.-P., Plattner, H.: Towards Integrating the Detection of Genetic Variants into an In-Memory Database.Proceedings of the International Conference on Big Data (2014).
     
  • Schapranow, M.-P., Plattner, H.: High-Performance In-Memory Genome Data Analysis: How In-Memory Database Technology Accelerates Personalized Medicine.Springer (2014).
     
  • Schapranow, M.-P., Klinghammer, K., Fähnrich, C., Plattner, H.: An Optimized Research Process for Real-time Drug Response Analysis.The Third International Conference on Global Health Challenges (2014).
     
  • Schapranow, M.-P., Klinghammer, K., Fähnrich, C., Plattner, H.: In-Memory Technology Enables Interactive Drug Response Analysis.16th International Conference on e-Health Networking, Applications and Services (Healthcom 2014) (2014).
     

2013

  • Schapranow, M.-P.: Krebsdatenbank auf Tablet und Smartphone.Best Practice Wireless in der Hauptstadtregion Berlin-Brandenburg, pp. 48-49 (2013).
     
  • Schapranow, M.-P., Plattner, H., Meinel, C.: Applied In-Memory Technology for High-Throughput Genome Data Processing and Real-time Analysis.System on Chip (!SoC) Devices in Telemedicine from !LABoC to High Resolution Images, pp. 35-42 (2013).
     
  • Haeger, F., Plattner, H., Schapranow, M.-P.: High-Performance In-Memory Genome Project: A Platform for Integrated Real-Time Genome Data Analysis.Proceedings of the 2nd International Conference on Global Health Challenges. pp. 5-10 (2013).
     
  • Schapranow, M.-P., Plattner, H.: HIG – An In-memory Database Platform Enabling Real-time Analyses of Genome Data.Proceedings of the International Conference on Big Data. pp. 691-696 (2013).
     
  • Schapranow, M.-P., Meinel, C., Plattner, H.: Applied In-Memory Technology to High Throughput Genome Data Processing.Proceedings of XXI Winter Course of the CATAI (CATAI 2013). pp. 35-42. , Canary Islands, Spain (2013).
     
  • Schapranow, M.-P., Plattner, H.: In-Memory Technology Enables History-Based Access Control for RFID-Aided Supply Chains.The Secure Information Society: Ethical, Legal and Political Challenges, pp. 187-213 (2013).
     
  • Schapranow, M.-P., Meinel, C., Plattner, H.: Big Data soll Genom-Analysen schneller voranbringen.Krankenhaus-IT Journal, p. 26. (2013).
     
  • Schapranow, M.-P., Plattner, H., Tosun, C., Regenbrecht, C.: Mobile Real-time Analysis of Patient Data for Advanced Decision Support in Personalized Medicine.The 5th International Conference on eHealth, Telemedicine, and Social Medicine, pp. 129-136 (2013).
     
  • Schapranow, M.-P.: Real-time Security Extensions for EPCglobal Networks: Case Study for the Pharmaceutical Industry.In-Memory Data Management Research, Springer (2013).
     

2012

  • Schapranow, M.-P., Meinel, C., Plattner, H.: Datenbanktechnologie beflügelt personalisierte Medizin.Laborwelt Nr. 4/2012, 13. Jahrgang. (2012).
     
  • Plattner, H., Meinel, C., Schapranow, M.-P.: Blitzschnelle Datenanalysen für die personalisierte Medizin der Zukunft – Interdisziplinäre Impulse aus Potsdam und Berlin.Themenbroschüre 2012 Gesundheitsstandort Berlin-Brandenburg. (2012).
     
  • Müller, J., Schapranow, M.-P., Zeier, A., Plattner, H.: Secure RFID-Enablement in Modern Companies: A Case Study of the Pharmaceutical Industry.Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions, pp. 507-539, IGI Global (2012).
     

2011

  • Schapranow, M.-P., Zeier, A., Plattner, H., Müller, J., Lorenz, M.: Discovery Services in the EPC Network.Designing and Deploying RFID Applications, pp. 109-130, INTECH Press (2011).
     
  • Schapranow, M.-P., Müller, J., Zeier, A., Plattner, H.: What are Authentic Pharmaceuticals Worth?Designing and Deploying RFID Applications, pp. 204-220, INTECH Press (2011).
     
  • Schapranow, M.-P., Zeier, A., Plattner, H.: A Formal Model for Enabling RFID in Pharmaceutical Supply Chains.44th Hawaii International Conference on System Sciences (HICSS). IEEE (2011).
     
  • Schapranow, M.-P., Zeier, A., Plattner, H.: Security Extensions for Improving Data Security of Event Repositories in EPCglobal Networks.The 9th International Conference on Embedded and Ubiquitous Computing pp. 213-220 (2011).
     
  • Schapranow, M.-P., Lorenz, M., Zeier, A., Plattner, H.: License-based Access Control in EPCglobal Networks.The 7th European Workshop on RFID Systems and Technologies, pp. 1-7 (2011).
     
  • Schapranow, M.-P., Zeier, A., Leupold, F., Schubotz, T.: Securing EPCglobal Object Name Service -- Privacy Enhancements for Anti-counterfeiting.2nd International Conference on Intelligent Systems, Modeling and Simulation, pp. 332-337 (2011).
     
  • Lorenz, M., Müller, J., Schapranow, M.-P., Zeier, A.: A Distributed EPC Discovery Service based on Peer-to-peer Technology.Proceedings of the RFID SysTech 2011, 7th European Workshop on Smart Objects: Systems, Technologies and Applications, Dresden. pp. 1 - 7. VDE (2011).
     
  • Zeier, A., Plattner, H., Schapranow, M.-P., Müller, J.: Costs of Authentic Pharmaceuticals: Research on Qualitative and Quantitative Aspects of Enabling Anti-counterfeiting in RFID-aided Supply Chains.Personal and Ubiquitous Computing, Volume 16, Issue 3.271-289 (2011).
     
  • Schapranow, M.-P., Fähnrich, C., Zeier, A., Plattner, H.: Simulation of RFID-aided Supply Chains: Case Study of the Pharmaceutical Supply Chain.Third International Conference on Computational Intelligence, Modelling and Simulation, pp. 340-345 (2011).
     

2010

  • Schapranow, M.-P., Kühne, R., Zeier, A.: Real-Time Billing in Smart Grid Infrastructures.Power and Energy Student Summit 2010 - Integration of Renewable Energies into the Grid (2010).
     
  • Müller, J., Schapranow, M.-P., Pöpke, C., Urbat, M., Zeier, A., Plattner, H.: Best Practices for Rigorous Evaluation of RFID Software Components.Proceedings of the 6th European Workshop on RFID Systems and Technologies (RFID !SysTech'10), Ciudad Real, Spain (2010).
     
  • Schapranow, M.-P., Müller, J., Zeier, A., Plattner, H.: RFID Event Data Processing -- An Architecture for Storing and Searching.Proceedings of the 4th International Workshop on RFID Technology - Concepts, Applications, Challenges, Funchal, Madeira, Portugal (2010).
     
  • Schapranow, M.-P., Zeier, A., Plattner, H.: A Dynamic Mutual RFID Authentication Model Preventing Unauthorized Third Party Access.The 4th International Conference on Network and System Security, , pp. 371-376 (2010).
     
  • Schapranow, M.-P., Nagora, M., Zeier, A.: CoMoSeR: Cost Model for Security-Enhanced RFID-Aided Supply Chains.The 18th International Conference on Software, Telecommunication and Computer Networks, pp. 11-16 (2010).
     
  • Schapranow, M.-P., Geller, F., Lorenz, M., Müller, J., Kowark, T., Zeier, A.: Assessment of Communication Protocols in the EPC Network: Replacing Textual SOAP and XML with Binary Google Protocol Buffers Encoding.17th IEEE International Conference on Industrial Engineering and Engineering Management (IE&EM), Xiamen, China (2010).
     
  • Schapranow, M.-P., Kühne, R., Zeier, A.: Enabling Real-Time Charging for Smart Grid Infrastructures using In-Memory Databases.1st IEEE LCN Workshop on Smart Grid Networking Infrastructure (2010).
     

2009

  • Schapranow, M.-P., Müller, J., Krüger, J., Hofmann, P., Zeier, A.: Integration of RFID Technology is a Key Enabler for Demand-Driven Supply Network.The IUP Journal of Supply Chain Management, Volume 6, Nos. 3 & 4, pp. 57-74. (2009).
     
  • Schapranow, M.-P., Müller, J., Enderlein, S., Helmich, M., Zeier, A.: Low-Cost Mutual RFID Authentication Model Using Predefined Password Lists.The 16th International Conference on Industrial Engineering and Engineering Management, pp. 889-893 (2009).
     
  • Müller, J., Faust, M., Schwalb, D., Schapranow, M.-P., Zeier, A., Plattner, H.: A Software as a Service RFID Middleware for Small and Medium-sized Enterprises.Proceedings of the 5th European Workshop on RFID Systems and Technologies (RFID !SysTech'09), Bremen, Germany (2009).
     
  • Schapranow, M.-P., Krüger, J., Borovskiy, V., Zeier, A., Plattner, H.: Data Loading & Caching Strategies in Service-Oriented Enterprise Applications.Proceedings of IEEE Congress on Services (SERVICES 2009), Los Angeles, CA, USA (2009).
     
  • Borovskiy, V., Müller, J., Schapranow, M.-P., Zeier, A.: Ensuring Service Backwards Compatibility with Generic Web Services.Proceedings of ICSE 2009, PESOS Workshop, Vancouver, Canada (2009).
     
  • Zeier, A., Schapranow, M.-P., Krüger, J., Uflacker, M., Müller, J.: noFilis CrossTalk 2.0 as Device Management Solution, Experiences while Integrating RFID Hardware into SAP Auto-ID Infrastructure.Proceedings of the 16th International Conference on Industrial Engineering and Engineering Management (IE&EM), Beijing, China (2009).
     
  • Schapranow, M.-P., Zeier, A., Borovskiy, V., Müller, J.: Binary Search Tree Visualization Algorithm.16th International Conference on Industrial Engineering and Engineering Management (IE&EM), Beijing, China (2009).
     
  • Müller, J., Schapranow, M.-P., Helmich, M., Enderlein, S., Zeier, A.: RFID Middleware as a Service - Enabling Small and Medium-sized Enterprises to Participate in the EPC Network.16th International Conference on Industrial Engineering and Engineering Management (IE&EM), Beijing, China (2009).
     
  • Schapranow, M.-P., Müller, J., Zeier, A., Plattner, H.: Security Aspects in Vulnerable RFID-Aided Supply Chains.Proceedings of the 5th European Workshop on RFID Systems and Technologies, Bremen (2009).
     

2008

  • Schapranow, M.-P., Krüger, J., Müller, J.: Smart Enterprise Widgets: Little Helpers with a Big Impact.SAP INFO (online). (2008).
     
  • Krüger, J., Grund, M., Schaffner, J., Schapranow, M.-P., Bog, A.: Shared Table Access Pattern Analysis for Multi-Tenant Applications.IEEE Symposium on Advanced Management of Information for Globalized Enterprises (AMIGE'08), Tianjin, China (2008).
     
  • Grund, M., Schaffner, J., Schapranow, M.-P., Bog, A., Krüger, J.: Operational Reporting Using Navigational SQL.IEEE Symposium on Advanced Management of Information for Globalized Enterprises (AMIGE'08), Tianjin, China (2008).
     
  • Schapranow, M.-P., Krüger, J.: HPI Students Learn with SAP Enterprise Services.SAP INFO (online). (2008).
     
  • Schapranow, M.-P., Grund, M., Krüger, J., Schaffner, J., Bog, A.: Combining Advantages - Unified Data Stores in Global Enterprises.IEEE Symposium on Advanced Management of Information for Globalized Enterprises (AMIGE'08), Tianjin, China (2008).