Mining Big and Fast Data: Algorithms and Optimizations for Real-Time Data Processing

Sammanfattning: In the last decade, real-time data processing has attracted much attention from both academic community and industry, as the meaning of big data has evolved to incorporate as well the speed of data. The massive and rapid production of data comes via numerous services, i.e., Web, social networks, Internet of Things (IoT) and mobile devices. For instance, global positioning systems are producing continuous data points using various location-based services. IoT devices are continuously monitoring variety of parameters, like temperature, heart beats, and others, and sending the data over the network. Moreover, part of the data produced by these real-time services is linked-data that requires tools for streaming graph analytics. Real-time graphs are ubiquitous in many fields, from the web advertising to bio-analytics. Developing analytical tools to process this amount of information at a real-time is challenging, yet extremely essential, for developing new services in areas such as web analytics, e-health and marketing.Distributed stream processing engines (dspes) are often employed for real-time data processing, as they distribute work to many machines to achieve the required performance guarantees, i.e., low latency and high throughput. However, the scalability of dspes is often questioned when the input streams are skewed or the underlying resources are heterogeneous. In this thesis, we perform a scalability study for dspes. In particular, we study the load- balancing problem for dspes, which is caused by the skewness in the workload and heterogeneity in the cluster. In doing so, we develop several efficient and accurate algorithms to reduce the load imbalance in a distributed system. Moreover, our algorithms are integrated into Apache Storm, which is an open source stream processing framework.Another dimension of real-time data processing involves developing novel algorithms for graph-related problems. The later part of the thesis presents several algorithms for evolving graphs. One of the most interesting features of real-world networks is the presence of community structure, which divides a network into groups of nodes with dense connections internally and sparse connections between groups. We study the community detection problem in the fully dynamic settings by formulating it as a top-k densest subgraph problem. In doing so, we achieve an extremely efficient approximation algorithm that scales to graphs with billions of edges. Further, we study the top-k graph pattern-mining problem in fully dynamic settings and develop a probabilistic algorithm using reservoir sampling. We provide the theoretical analysis for the proposed algorithms and show via empirical evaluation that our algorithms achieve up to several orders of magnitude improvement compared to the state-of-the-art algorithm.