Engineering an Open Web Syndication Interchange with Discovery and Recommender Capabilities

Web syndication has become a popular means of delivering relevant information to people online but the complexity of standards, algorithms and applications pose considerable challenges to engineers.  This paper describes the design and development of a novel Web-based syndication intermediary called InterSynd and a simple Web client as a proof of concept. We developed format-neutral middleware that sits between content sources and the user. Additional objectives were to add feed discovery and recommendation components to the intermediary. A search-based feed discovery module helps users find relevant feed sources. Implicit collaborative recommendations of new feeds are also made to the user. The syndication software built uses open standard XML technologies and the free open source libraries. Extensibility and re-configurability were explicit goals. The experience shows that a modular architecture can combine open source modules to build state-of-the-art syndication middleware and applications. The data produced by software metrics indicate the high degree of modularity retained

Big continuous data: dealing with velocity by composing event streams

International audienceThe rate at which we produce data is growing steadily, thus creating even larger streams of continuously evolving data. Online news, micro-blogs, search queries are just a few examples of these continuous streams of user activities. The value of these streams relies in their freshness and relatedness to on-going events. Modern applications consuming these streams need to extract behaviour patterns that can be obtained by aggregating and mining statically and dynamically huge event histories. An event is the notification that a happening of interest has occurred. Event streams must be combined or aggregated to produce more meaningful information. By combining and aggregating them either from multiple producers, or from a single one during a given period of time, a limited set of events describing meaningful situations may be notified to consumers. Event streams with their volume and continuous production cope mainly with two of the characteristics given to Big Data by the 5V’s model: volume & velocity. Techniques such as complex pattern detection, event correlation, event aggregation, event mining and stream processing, have been used for composing events. Nevertheless, to the best of our knowledge, few approaches integrate different composition techniques (online and post-mortem) for dealing with Big Data velocity. This chapter gives an analytical overview of event stream processing and composition approaches: complex event languages, services and event querying systems on distributed logs. Our analysis underlines the challenges introduced by Big Data velocity and volume and use them as reference for identifying the scope and limitations of results stemming from different disciplines: networks, distributed systems, stream databases, event composition services, and data mining on traces

Enhancing performance and expressibility of complex event processing using binary tree-based directed graph

In various domains, applications are required to detect and react to complex situations accordingly. In response to the demand for matching receiving events to complex patterns, several event processing systems have been developed. However, there are just a few of them considered both performance and expressibility of event matching as focusing only on performance can cause negative effect on the expressibility or vice versa. This research develops a fast adaptive event matching system (FAEM), a new event matching system to improve expressibility and performance measures (throughput and end-to-end latency). This system is designed and developed based on a novel binary tree-based directed graph (BTDG) as a unified basis for event-matching. The proposed system transforms a user-defined query into a set of system objects including buffers, conditions on buffers, cursors, and join operators (non-kleene and kleene operators) and arranges these objects on a BTDG. Provided BTDG the enhancement in performance of non-kleene operators applied through developing a batch removal method to remove the events that are located out of time-window, and an actual time window (ATW) which can improve performance of event matching. To improve performance of kleene operators, this research introduces a twin algorithms for kleene operator which is match to BTDG. These two kleene algorithms apply grouping on events and reduce the number of intermediate results and apply combination algorithm in final stage. Transformation of queries containing join operators into BTDG enhances the expressibility of the proposed CEP system

LIME: A Coordination Middleware Supporting Mobility of Agents and Hosts

LIME (Linda in a Mobile Environment) is a middleware supporting the development of applications that exhibit physical mobility of hosts, logical mobility of agents, or both. LIME adopts a coordination perspective inspired by work on the Linda model. The context for computation, represented in Linda by a globally accessible, persistent tuple space, is refined in LIME to transient sharing of identically-named tuple spaces carried by individual mobile units. Tuple spaces are also extended with a notion of location and programs are given the ability to react to specified states. The resulting model provides a minimalist set of abstractions that promise to facilitate rapid and dependable development of mobile applications. In this paper, we illustrate the model underlying LIME, provide a formal semantic characterization for the operations it makes available to the application developer, present its current design and implementation, and discuss lessons learned in developing applications that involve physical mobility

Towards a fully mobile publish/subscribe system

93 p.This PhD thesis makes contributions to support mobility and fault tolerance in a publish/subscribe system. Two protocols are proposed in order to support mobility of all devices in the system, including inside the event notification service. The protocols are designed with the idea that any change due to mobility is completely beyond our control and ability to predict. Moreover, the proposed solutions do not need to know neither the amount of nodes in the system nor their identities before starting, the system is able to adapt to new devices or disconnections and is able to keep operating correctly in a partitioned network. To do so we extend a previously proposed framework called Phoenix that already supported client mobility. Both protocols use a leader election mechanism to create a communication tree in a highly dynamic environment, and use a characteristic of that algorithm to detect topology changes and migrate nodes accordingly

Speculative reordering for a latency-optimized privacy protection in complex event processing

With increasing number of applications in Internet of Things (IoT), Complex Event Processing (CEP) has already become one of the state-of-the-art technologies recently. In CEP, privacy needs to be considered carefully because events with user’s sensitive information may be exposed to outside world. However, most privacy issues in CEP mainly focus on attribute-based events without considering pattern-based events. There are two important works for pattern-based privacy in CEP: suppression and re-ordering. The former suppresses events belonging to private patterns while the later tends to reorder them. The re-ordering mechanism shows better performance in terms of QoS, but the latency would be long when the size of window increases. Also, the re-ordering strategy is performed only at the end of the windows. In this thesis, we extend the Re-ordering strategy by using speculation based on Markov chains, so we start speculating whether the private pattern occurs in current window before the end of the window. If the private pattern is predicted to occur, we then already re-order events that are part of private patterns. Additionally, the top-k preserving algorithm is introduced for preserving public patterns. Our evaluation results show that we maintain nearly 80 % utility when compared to the normal re-ordering strategy. From our experiments, it is seen that we can eliminate the time taken for re-ordering completely if the window size is greater than 3 ms

Trusted content-based publish/subscribe trees

Publish/Subscribe systems hold strong assumptions of the expected behaviour of clients and routers, as it is assumed they all abide by the matching and routing protocols. Assumptions of implicit trust between the components of the publish/subscribe infrastructure are acceptable where the underlying event distribution service is under the control of a single or multiple co-operating administrative entities and contracts between clients and these authorities exist, however there are application contexts where these presumptions do not hold. In such environments, such as ad hoc networks, there is the possibility of selfish and malicious behaviour that can lead to disruption of the routing and matching algorithms. The most commonly researched approach to security in publish/subscribe systems is role-based access control (RBAC). RBAC is suitable for ensuring confidentiality, but due to the assumption of strong identities associated with well defined roles and the absence of monitoring systems to allow for adaptable policies in response to the changing behaviour of clients, it is not appropriate for environments where: identities can not be assigned to roles in the absence of a trusted administrative entity; long-lived identities of entities do not exist; and where the threat model consists of highly adaptable malicious and selfish entities. Motivated by recent work in the application of trust and reputation to Peer-to-Peer networks, where past behaviour is used to generate trust opinions that inform future transactions, we propose an approach where the publish/subscribe infrastructure is constructed and re-configured with respect to the trust preferences of clients and routers. In this thesis, we show how Publish/Subscribe trees (PSTs) can be constructed with respect to the trust preferences of publishers and subscribers, and the overhead costs of event dissemination. Using social welfare theory, it is shown that individual trust preferences over clients and routers, which are informed by a variety of trust sources, can be aggregated to give a social preference over the set of feasible PSTs. By combining this and the existing work on PST overheads, the Maximum Trust PST with Overhead Budget problem is defined and is shown to be in NP-complete. An exhaustive search algorithm is proposed that is shown to be suitable only for very small problem sizes. To improve scalability, a faster tabu search algorithm is presented, which is shown to scale to larger problem instances and gives good approximations of the optimal solutions. The research contributions of this work are: the use of social welfare theory to provide a mechanism to establish the trustworthiness of PSTs; the finding that individual trust is not interpersonal comparable as is considered to be the case in much of the trust literature; the Maximum Trust PST with Overhead Budget problem; and algorithms to solve this problem