We identify an opportunity to share data among mobile apps hosted in the cloud, thus helping users improve their mobile experience, while resulting in cost savings for the cloud provider. In this work, we propose a platform for sharing data among mobile apps hosted in the cloud. A “sharing ” is specified by a triple consisting of: (a) a set of data sources to be shared, (b) a set of specified transforma-tions on the shared data, and (c) a staleness (freshness) requirement on the shared data. The platform addresses the following two main challenges: What sharings to admit into the system under a set of specified constraints, how to implement a sharing at a low cost while maintaining the desired level of staleness. We show that reductions in costs are achievable by exploiting the commonalities between the different sharings in the platform. Experimental evaluation is per-formed with a cloud platform containing 25 sharings among mo-bile apps with realistic datasets containing user, social, location and checkin data. Our platform is able to maintain the sharings with very few violations, even under a very high update rate. Our results show that our method results in a cost savings of over 35 % for the cloud provider, while enabling an improved mobile experience for users. 1.

Pattern queries are widely used in complex event processing (CEP) systems. Existing pattern matching techniques, however, can provide only limited performance for expensive queries in real-world applications, which may involve Kleene closure patterns, flexible event selection strategies, and events with imprecise timestamps. To support these expensive queries with high performance, we begin our study by analyzing the complexity of pattern queries, with a focus on the fundamental understanding of which features make pattern queries more expressive and at the same time more computationally expensive. This analysis allows us to identify performance bottlenecks in processing those expensive queries, and provides key insights for us to develop a series of optimizations to mitigate those bottlenecks. Microbenchmark results show superior performance of our system for expensive pattern queries while most state-of-the-art systems suffer from poor performance. A thorough case study on Hadoop cluster monitoring further demonstrates the efficiency and effectiveness of our proposed techniques.

Large-scale event systems are becoming increasingly popular in a variety of domains. Event pattern evaluation plays a key role in monitoring applications in these domains. Existing work on pattern evaluation, however, assumes that the occurrence time of each event is known precisely and the events from various sources can be merged into a single stream with a total or partial order. We observe that in real-world applications event occurrence times are often unknown or imprecise. Therefore, we propose a temporal model that assigns a time interval to each event to represent all of its possible occurrence times and revisit pattern evaluation under this model. In particular, we propose the formal semantics of such pattern evaluation, two evaluation frameworks, and algorithms and optimizations in these frameworks. Our evaluation results using both real traces and synthetic systems show that the event-based framework always outperforms the point-based framework and with optimizations, it achieves high efficiency for a wide range of workloads tested.