Error-Aware Density-Based Clustering of Imprecise Measurement Values

Manufacturing process development is under constant pressure to achieve a good yield for stable processes. The development of new technologies, especially in the field of photomask and semiconductor development, is at its phys-ical limits. In this area, data, e.g. sensor data, has to be collected and analyzed for each process in order to ensure process quality. With increasing complexity of manufactur-ing processes, the volume of data that has to be evaluated rises accordingly. The complexity and data volume exceeds the possibility of a manual data analysis. At this point, data mining techniques become interesting. The application of current techniques is complex because most of the data is captured with sensor measurement tools. Therefore, every measured value contains a specific error. In this paper we propose an error-aware extension of the density-based al-gorithm DBSCAN. Furthermore, we present some quality measures which could be utilized for further interpretation of the determined clustering results. With this new cluster algorithm, we can ensure that masks are classified into the correct cluster with respect to the measurement errors, thus ensuring a more likely correlation between the masks

A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifth-generation mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system. Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level. © 2019, Springer Science+Business Media, LLC, part of Springer Nature

Reputation-aware Trajectory-based Data Mining in the Internet of Things (IoT)

Internet of Things (IoT) is a critically important technology for the acquisition of spatiotemporally dense data in diverse applications, ranging from environmental monitoring to surveillance systems. Such data helps us improve our transportation systems, monitor our air quality and the spread of diseases, respond to natural disasters, and a bevy of other applications. However, IoT sensor data is error-prone due to a number of reasons: sensors may be deployed in hazardous environments, may deplete their energy resources, have mechanical faults, or maybe become the targets of malicious attacks by adversaries. While previous research has attempted to improve the quality of the IoT data, they are limited in terms of better realization of the sensing context and resiliency against malicious attackers in real time. For instance, the data fusion techniques, which process the data in batches, cannot be applied to time-critical applications as they take a long time to respond. Furthermore, context-awareness allows us to examine the sensing environment and react to environmental changes. While previous research has considered geographical context, no related contemporary work has studied how a variety of sensor context (e.g., terrain elevation, wind speed, and user movement during sensing) can be used along with spatiotemporal relationships for online data prediction. This dissertation aims at developing online methods for data prediction by fusing spatiotemporal and contextual relationships among the participating resource-constrained mobile IoT devices (e.g. smartphones, smart watches, and fitness tracking devices). To achieve this goal, we first introduce a data prediction mechanism that considers the spatiotemporal and contextual relationship among the sensors. Second, we develop a real-time outlier detection approach stemming from a window-based sub-trajectory clustering method for finding behavioral movement similarity in terms of space, time, direction, and location semantics. We relax the prior assumption of cooperative sensors in the concluding section. Finally, we develop a reputation-aware context-based data fusion mechanism by exploiting inter sensor-category correlations. On one hand, this method is capable of defending against false data injection by differentiating malicious and honest participants based on their reported data in real time. On the other hand, this mechanism yields a lower data prediction error rate

A Natural Experiment on Sick Pay Cuts, Sickness Absence, and Labor Costs

This study estimates the reform effects of a reduction in statutory sick pay levels on various outcome dimensions. A federal law reduced the legal obligation of German employers to provide 100 percent continued wages for up to six weeks per sickness episode to 80 percent. This measure increased the ratio of employees having no days of absence by about 7.5 percent. The mean number of absence days per year decreased by about 5 percent. The reform might have reduced total labor costs by about EUR1.5 billion per year which might have led to the creation of around 50,000 new jobs.

Area interpolation in presence of measurement error and an application to German administrative data

"In many situations the applied researcher wants to combine different data sources without knowing the exact link and merging rule. This paper considers different interpolation methods for interpolating attributes from German labor office districts to German counties and vice versa. In particular, we apply dasymetric weighting as an alternative to simple area weighting both of which are based on estimated intersection areas. Since these estimates can be spurious, our theoretical framework extends the well-known Goodchild and Lam (1980) approach to the presence of measurement error in the underlying maps. We also present conditions under which the choice of interpolation method does not matter and confirm the theoretical results with a simulation study. Our application to German administrative data suggests robustness of estimation results of interpolated attributes with respect to the choice of interpolation method. We deliver weighting matrices for regional data sources of the two largest German data producers." (Author's abstract, IAB-Doku) ((en)) Additional Information Anlage zum FDZ-Methodenreport Nr. 01/2006: Stata-Dateien mit den Gewichten: Gewichte.zipprozessproduzierte Daten, Statistisches Bundesamt, Landkreis, Bundesagentur für Arbeit, Arbeitsagenturbezirke, Datenqualität, Datenverarbeitung, Regionalgliederung, Kartierung, regionaler Vergleich, IAB-Beschäftigtenstichprobe, Wirtschaftsforschung, Arbeitsmarktforschung

Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference