Syed Gillani

IntelSCEP: Towards an Intelligent Semantic Complex Event Processing Framework for Prosumer-Oriented SmartGrid

Recognition of patterns and prediction of future events has become quite important in many application areas of Complex Event Processing (CEP). However, the state-of-art technologies cannot cope with the heterogeneity of event streams and employ historical and future events to predict new situations. Emerging applications like Prosumer-Oriented SmartGrid are spread across multiple domains and require a solution to handle large influx of heterogeneous streams and extract meaningful patterns to devise automated response. In this paper, we present a motivational scenario for such system, discuss some limitations of existing CEP systems and describe our work towards an intelligent semantically enriched CEP (IntelSCEP) system.

Continuous graph pattern matching over knowledge graph streams

Continuous Graph Pattern Matching (CGPM) is an extended version of the traditional GPM that is evaluated over Knowledge Graph (Kg) streams. It comes with additional constraints of scalability and near-to-real-time response, and is used in many applications such as real-time knowledge management, social networks and sensor networks. Hence, existing GPM solutions for static Kgs are not directly applicable in this setting. This paper studies continuous GPM over Kg streams for two different executional models: event-based and incremental. We first propose a query-based graph pruning technique to filter the unnecessary triples from a Kg event. The pruned events are materialized in a set of vertically partitioned tables. We then use a hybrid join-and-explore technique to further prune and finally match the triples within a Kg event. Considering the on-the-fly execution of queries over pruned Kg events, we use an automata-based model to guide the join and exploration process. This leads to an index-free solution optimised for streaming environments. Experimental results with both synthetic and real-world datasets confirm that our system outperforms the state-of-the-art solutions by (on average) one to two orders of magnitude, in terms of performance and scalability.

Upsortable: programming top-k queries over data streams

Top-k queries over data streams is a well studied problem. There exists numerous systems allowing to process continuous queries over sliding windows. At the opposite, non-append only streams call for ad-hoc solutions, e.g. tailor-made solutions implemented in a mainstream programming language. In the meantime, the Stream API and lambda expressions have been added in Java 8, thus gaining powerful operations for data stream processing. However, the Java Collections Framework does not provide data structures to safely and conveniently support sorted collections of evolving data. In this paper, we demonstrate Upsortable, an annotation-based approach that allows to use existing sorted collections from the standard Java API for dynamic data management. Our approach relies on a combination of pre-compilation abstract syntax tree modifications and runtime analysis of bytecode. Upsortable offers the developer a safe and time-efficient solution for developing top-k queries on data streams while keeping a full compatibility with standard Java.

High performance top-k processing of non-linear windows over data streams

This years DEBS Grand Challenge offers two very challenging queries over social networks data. These queries -- each for a different reason -- cannot be handled by traditional techniques and therefore call for the development of a specific architecture and data structures. In the first query, the novelty is the non-linearity of the expiration of the elements. Since a traditional sliding window is not suitable, we investigate here the data structures offering the best tradeoffs for all the required operations. In the second query, unlike traditional approaches where no persistent data is stored over the stream, we have to manage a friendship graph which is persistent throughout the system execution. Due to the centrality of this structure, a careful design is therefore required. The common point of the algorithmic approaches that we developed for both queries, is the overwhelming usage of bounds -- upper and lower --, in order execute expensive computations only when required. We devise, for the Query 1, a bound based on the score decay. For the Query 2, we use Turans theorem to limit the clique computation. The combination of lazy evaluation, careful implementation and thorough testing lead to the realization of an efficient streaming process system.

Top-K queries in RDF graph-based stream processing with actors

In this paper, we describe our novel system named as RGraSPA an RDF Graph-based Stream Processing with Actors, which adheres to the realm of RDF graph and knowledge reasoning, and uses an actor model for distribution of continuous queries. Furthermore, we present our approach to solve DEBS Grand Challenge by employing our system. RGraSPA uses RDF graph-based event model to encapsulate a set of triples and process them in continuous manner. We also present our synchronised structure traversal algorithm that uses Range tree to store results in a sorted view, where each node of the tree maintains a balanced Multimap Binary Search Tree (BST). The range of each node is adaptive and updated according to the incoming values and defined size of the Multimap BST for each node. In order to solve the DEBS challenge, we provide a formal method to calculate cell IDs from the longitude and latitude in a streaming fashion and use two Range trees for 10 most frequent routes and profitable areas. Our experimental results show that the query execution time can be optimised by carefully adjusting the cardinality values of Range tree. Our solution processes 1 year worth of RD-Fised data (372 GB) (approx 3.4 billion triples) for Taxis in 1.8 hours.