StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge

Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promises low-latency pattern detection on event streams. However, CEP systems need to be extended with Machine Learning (ML) capabilities such as online training and inference in order to be able to detect fuzzy patterns (e.g., outliers) and to improve pattern recognition accuracy during runtime using incremental model training. In this paper, we propose a distributed CEP system denoted as StreamLearner for ML-enabled complex event detection. The proposed programming model and data-parallel system architecture enable a wide range of real-world applications and allow for dynamically scaling up and out system resources for low-latency, high-throughput event processing. We show that the DEBS Grand Challenge 2017 case study (i.e., anomaly detection in smart factories) integrates seamlessly into the StreamLearner API. Our experiments verify scalability and high event throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS '17), 298-30

Macroeconomic Imbalances in EMU and the Eurosystem

Schulden; Finanzmarktkrise; Euro; Schuldenkrise; Europäische Wirtschafts- und Währungsunion

Why do households without children support local public schools? linking house price capitalization to school spending

While residents receive similar benefits from many local public expenditures, only about one-third of all households have children in the public schools. In this paper the authors argue that capitalization of school spending into house prices can encourage residents to support spending on schools, even if the residents themselves will never have children in the schools. To examine this hypothesis, the authors take advantage of differences across communities in the extent of house price capitalization based on the availability of land or population density. They show that fiscal variables and amenities are capitalized to a much greater extent in Massachusetts cities and towns with little available land and that these localities also spend more on schools. Next, the authors use data from school districts in 49 states to show that per pupil spending is positively related to population density, a proxy for the availability of land. Consistent with a model tying house price capitalization to school spending, the authors show that the positive correlation between density and spending persists only in locations with high homeownership rates. Communities with a higher percentage of residents above 65 years old have increased school expenditures only in places with high population densities, and this correlation grows for the percentage of elderly above 75 or 85 years old who have a shorter expected duration in their house. The positive relationship between percentage elderly and school spending is confined to central cities and suburbs of large metropolitan areas and does not exist in places where land for new construction may be easier to obtain. These results support models in which house price capitalization encourages more efficient provision of public services and provide an explanation for why some elderly residents might support local spending on schools.Education

Why Do Households Without Children Support Local Public Schools?

While residents receive similar benefits from many local government programs, only about one-third of all households have children in public schools. We argue that capitalization of school spending into house prices can encourage residents to support spending on schools, even if the residents themselves will never have children in schools. We identify a proxy for the extent of capitalization—the supply of land available for new development—and show that in response to a plausibly exogenous spending shock in Massachusetts, towns with little undeveloped land have larger changes in house prices, but smaller changes in quantity (construction). Towns with little available land also spend more on schools. We extend these results using data from school districts in 46 states, showing that per pupil spending is positively related to the percentage of developed land. This positive correlation persists only in districts where the median resident is a homeowner and is stronger in districts with more elderly residents who do not use school services and have a shorter expected duration in their home. These findings support models in which house price capitalization encourages more efficient provision of public services and may explain why some elderly residents support school spending.

Seafloor Characterization from Spatial Variation of Multibeam Backscatter vs. Best Estimated Grazing Angle

Backscatter vs. grazing angle, which can be extracted from multibeam backscatter data, depends on characteristics of the multibeam system and the angular responses of backscatter that are characteristic of different seafloor properties, such as sediment hardness and roughness. Changes in backscatter vs. grazing angle that are contributed by the multibeam system normally remain fixed over both space and time. Therefore, they can readily be determined and removed from backscatter data. The component of backscatter vs. grazing angle due to the properties of sediments varies from location to location, as the sediment changes. The sediment component of variability can be inferred using the redundant observations from different grazing angles in several small sections of seafloor assuming that the sediment property is uniform in any given section of seafloor yet varies from one section of the seafloor to another. The multibeam data used in this research is from the ONR sponsored STRATAFORM project. The location of the study area was the mid-outer continental shelf off New Jersey. A small subset (11 x 17 km) of the NJ multibeam survey was selected and divided into 1380 equal working cells. The backscatter vs. grazing angle dependence for each cell was computed by averaging backscatter data by the corresponding grazing angles using all data with the same grazing angle from different survey lines. Taking into account the effects of local topographic variations of the seabed, the estimated grazing angle for each beam has been computed from available adjacent soundings within a 15-meter radius using a least squares fit with a Butterfly weighting function. A graphic interface was developed to ease evaluation of the spatial variation of backscatter vs. grazing angle. With a mouse click, images based on different subsets of the data can be compared throughout the survey area. The subsets were created from specific grazing angles. These images show significant variations between nadir and off-nadir beams. Variations apparent in the images may provide some indication of the sediment (or seafloor) characteristics, which can be compared to ground truth data (sediment grain size) and measured values such as velocity and density