Yagil Engel

Towards proactive event-driven computing

Event driven architecture is a paradigm shift from traditional computing architectures which employ synchronous, request-response interactions. In this paper we introduce a conceptual architecture for what can be considered the next phase of that evolution: proactive event-driven computing. Proactivity refers to the ability to mitigate or eliminate undesired future events, or to identify and take advantage of future opportunities, by applying prediction and automated decision making technologies. We investigate an extension of the event processing conceptual model and architecture to support proactive event-driven applications, and propose the main building blocks of a novel architecture. We first describe several extensions to the existing event processing functionality that is required to support proactivity; next, we extend the event processing agent model to include two more type of agents: predictive agents that may derive future uncertain events based on prediction models, and proactive agents that compute the best proactive action that should be taken. Those building blocks are demonstrated through a comprehensive scenario that deals with proactive decision making, ensuring timely delivery of critical material for a production plant.

Predictive Monitoring of Heterogeneous Service-Oriented Business Networks: The Transport and Logistics Case

Future service technology will provide an unprecedented access to operational data, which opens up novel opportunities for monitoring, controlling and managing service- oriented business processes. Amongst these opportunities, we consider predictive monitoring to be a major lever for increased efficiency, effectiveness and sustainability in future business networks. Predictive monitoring means that critical events, potential deviations and unplanned situations can be anticipated and proactively managed and mitigated along the execution of business processes. This paper demonstrates the potential of predictive monitoring in practice. We focus on transport & logistics as a major industry sector -- accounting for between 10% to 20% of a countrys Gross Domestic Product. Based on widely adopted standards and real operational data, we empirically support the relevance of key issues faced in that industry sector, such as late cancellations of transport bookings and delayed deliveries. As a solution, we describe the design of a novel, cloud- and services-based collaboration and integration platform. Based on this platform we develop short-term prediction capabilities allowing to proactively manage and mitigate the identified issues in the transport & logistics industry, thus promising to increase business efficiency and sustainability.

Multiattribute auctions based on generalized additive independence

We develop multiattribute auctions that accommodate generalized additive independent (GAI) preferences. We propose an iterative auction mechanism that maintains prices on potentially overlapping GAI clusters of attributes, thus decreases elicitation and computational burden, and creates an open competition among suppliers over a multidimensional domain. Most significantly, the auction is guaranteed to achieve surplus which approximates optimal welfare up to a small additive factor, under reasonable equilibrium strategies of traders. The main departure of GAI auctions from previous literature is to accommodate non-additive trader preferences, hence allowing traders to condition their evaluation of specific attributes on the value of other attributes. At the same time, the GAI structure supports a compact representation of prices, enabling a tractable auction process. We perform a simulation study, demonstrating and quantifying the significant efficiency advantage of more expressive preference modeling. We draw random GAI-structured utility functions with various internal structures, generate additive functions that approximate the GAI utility, and compare the performance of the auctions using the two representations. We find that allowing traders to express existing dependencies among attributes improves the economic efficiency of multiattribute auctions.

Directional Decomposition of Multiattribute Utility Functions

Several schemes have been proposed for compactly representing multiattribute utility functions, yet none seems to achieve the level of success achieved by Bayesian and Markov models for probability distributions. In an attempt to bridge the gap, we propose a new representation for utility functions which follows its probabilistic analog to a greater extent. Starting from a simple definition of marginal utility by utilizing reference values, we define a notion of conditional utility which satisfies additive analogues of the chain rule and Bayes rule. We farther develop the analogy to probabilities by describing a directed graphical representation that relies on our concept of conditional independence. One advantage of this model is that it leads to a natural structured elicitation process, very similar to that of Bayesian networks.

Future internet technology for the future of transport and logistics

International freight transport is the foundation of global trade, representing a large and growing industry where various stakeholders collaborate to transport goods around the world. The ICT infrastructures currently employed throughout logistics business networks are limited and the use of manual systems is common. This drastically hampers the operational efficiency of logistic service providers, carriers, and the various other stakeholders involved in transport processes. This paper presents an initial conceptual architecture for an ICT platform to overcome these deficiencies. The architecture is built on top of Future Internet technologies that provide generic capabilities for the efficient and effective development of cloud-based applications based on the Internet of Services, Internet of Things, and Internet of Contents with integrated security and privacy mechanisms.

Average-case analysis of VCG with approximate resource allocation algorithms

The Vickrey-Clarke-Groves (VCG) mechanism offers a general technique for resource allocation with payments, ensuring allocative efficiency while eliciting truthful information about preferences. However, VCG relies on exact computation of an optimal allocation of resources, a problem which is often computationally intractable, and VCG that uses an approximate allocation algorithm no longer guarantees truthful revelation of preferences. We present a series of results for computing or approximating an upper bound on agent incentives to misreport their preferences. Our first key result is an incentive bound that uses information about average (not worst-case) performance of an algorithm, which we illustrate using combinatorial auction data. Our second result offers a simple sampling technique for amplifying the difficulty of computing a utility-improving lie. An important consequence of our analysis is an argument that using state-of-the-art algorithms for solving combinatorial allocation problems essentially eliminates agent incentives to lie.

Average-case analysis of mechanism design with approximate resource allocation algorithms

Mechanism design provides a useful practical paradigm for competitive resource allocation when agent preferences are uncertain. Vickrey-Clarke-Groves (VCG) mechanism offers a general technique for resource allocation with payments, ensuring allocative efficiency while eliciting truthful information about preferences. However, VCG relies on exact computation of optimal allocation of resources, a problem which is often computationally intractable. Using approximate allocation algorithms in place of exact algorithms gives rise to a VCG-based mechanism, which, unfortunately, no longer guarantees truthful revelation of preferences. Our main result is an average-case bound, which uses information about average, rather than worst-case, performance of an algorithm. We show how to combine the resulting bound with simulations to obtain probabilistic confidence bounds on agent incentives to misreport their preferences and illustrate the technique using combinatorial auction data. One important consequence of our analysis is an argument that using state-of-the-art algorithms for solving combinatorial allocation problems essentially eliminates agent incentives to misreport their preferences.