We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Privacy and Freedom.

We address the problem of failure diagnosis in discrete event systems with decentralized information. We propose a coordinated decentralized architecture consisting of local sites communicating with a coordinator that is responsible for diagnosing the failures occurring in the system. We extend the notion of diagnosability, originally introduced in Sampath et al. (1995) for centralized systems, to the proposed coordinated decentralized architecture. We specify three protocols that realize the proposed architecture; each protocol is defined by the diagnostic information generated at the local sites, the communication rules used by the local sites, and the coordinator‘s decision rule. We analyze the diagnostic properties of each protocol. We also state and prove conditions for a language to be diagnosable under each protocol. These conditions are checkable off-line. The on-line diagnostic process is carried out using the diagnosers introduced in Sampath et al. (1995) or a slight variation of these diagnosers. The key features of the proposed protocols are: (i) they achieve, each under a set of assumptions, the same diagnostic performance as the centralized diagnoser; and (ii) they highlight the ’’performance vs. complexity‘‘ tradeoff that arises in coordinated decentralized architectures. The correctness of two of the protocols relies on some stringent global ordering assumptions on message reception at the coordinator‘s site, the relaxation of which is briefly discussed.

Coordinated Decentralized Protocols for Failure Diagnosisof Discrete Event Systems

Techniques for providing friction-free transactions using geolocation and user identifiers are described herein. These techniques may ascertain a user's location based on a location of a mobile device. A transaction between the user and a merchant may be completed with zero or minimal input from the user based on the geolocation of the mobile device and the user identifiers. In some implementations, a transaction initiated earlier is completed when the mobile device arrives at the merchant. Additionally, a parent-child or similar relationship may be established between multiple devices. Security on the mobile device based may be provided by biometric identification and calculation of variance from regular movement patterns. Advertisements may be sent to the mobile device based on bids from merchants near to the mobile device. Promotions may be sent to the mobile device when more than a threshold number of mobile devices are located at the same merchant.

User profile and geolocation for efficient transactions

In this paper we present an efficient approach for the fault detection of discrete event systems using Petri nets. We assume that some of the transitions of the net are unobservable, including all those transitions that model faulty behaviors. We prove that the set of all possible firing sequences corresponding to a given observation can be described as follows. First a set of basis markings corresponding to the observation are computed together with the minimal set of transitions firings that justify them. Any other marking consistent with the observation must be reachable from a basis marking by firing only unobservable transitions. For the computation of the set of basis markings we propose a simple tabular algorithm and use it to determine a basis reachability tree that can be used as a diagnoser.

/pdf/fault-detection-for-discrete-event-systems-using-petri-nets-2b1pfist6s.pdf

Fault detection for discrete event systems using Petri nets with unobservable transitions

We address the problem of alarm correlation in large distributed systems. The key idea is to make use of the concurrence of events in order to separate and simplify the state estimation in a faulty system. Petri nets and their causality semantics are used to model concurrency. Special partially stochastic Petri nets are developed, that establish some kind of equivalence between concurrence and independence. The diagnosis problem is defined as the computation of the most likely history of the net given a sequence of observed alarms. Solutions are provided in four contexts, with a gradual complexity on the structure of observations.

/pdf/fault-detection-and-diagnosis-in-distributed-systems-an-14bmi80dy0.pdf

Fault Detection and Diagnosis in Distributed Systems: An Approach by Partially Stochastic Petri Nets

Today most of existing personalization systems (e.g content recommenders, or targeted ad) focus on individual users and ignore the social situation in which the services are consumed However, many human activities are social and involve several individuals whose tastes and expectations must be taken into account by the service providers When a group profile is not available, different profile aggregation strategies can be applied to recommend adequate content and services to a group of users based on their individual profiles In this paper, we consider an approach intended to determine the factors that influence the choice of an aggregation strategy We present a preliminary evaluation made on a real large-scale dataset of TV viewings, showing how group interests can be predicted by combining individual user profiles through an appropriate strategy The conducted experiments compare the group profiles obtained by aggregating individual user profiles according to various strategies to the “reference” group profile obtained by directly analyzing group consumptions.

Analysis of strategies for building group profiles

This paper presents a new use of safe Petri nets in the field of distributed discrete event systems, with application to telecommunication network management. This study has in its long range objectives to provide a generic supervisor, which can be easily distributed on a set of sensors. Petri nets are used to provide both a model and an algorithm in fault management domain. Key features of our approach are (1) we take advantage of the ability of Petri Nets to model concurrency in distributed systems, (2) we refuse using the marking graph in our algorithms in order to avoid state explosion and thus rely instead in the so-called partial order semantics of Petri nets, and (3) our algorithms use net unfolding techniques and extend them to the probabilistic case by providing a generalized Viterbi algorithm. This paper concentrates on application, motivations, and modelling.

A Petri net approach to fault detection and diagnosis in distributed systems. I. Application to telecommunication networks, motivations, and modelling

The present subject matter discloses a system and a method for privacy protection to protect the confidential and personal information of end users using a client device (108) to avail services recommended by a service provider (110). In one embodiment, a privacy protection system (102)for recommendation services comprises a processor (202) and a memory (204) coupled to the processor (204). The memory (204) comprises a interest group aggregator module (112)having at least one interest group aggregator, each of the at least one interest group aggregator configured to collate a plurality of segments of profile information pertaining to a plurality of end users categorized in a interest group based on a interest profile of each of the plurality of end users.

Privacy protection in recommendation services

Method(s) and system(s) for providing privacy to personal information of end users while utilizing recommendation services and personalized content are described. According to the present subject matter, the system(s) implement the described method(s) for providing privacy to personal information of end users. The method for privacy protection includes receiving user consumption data associated with one or more end users where the user consumption data comprises at least a slice of interest profile and associated interest group id of the slice of interest profile; and where the user consumption data is received through a network anonymization layer. The method may further include routing the user consumption data to an interest group aggregator node from amongst a plurality of intermediary nodes based on distributed hash table routing mechanism, and wherein the interest group aggregator node is associated with the interest group id present in the user consumption data.

Armen Aghasaryan

Papers

Fault Detection and Diagnosis in Distributed Systems: An Approach by Partially Stochastic Petri Nets

Analysis of strategies for building group profiles

A Petri net approach to fault detection and diagnosis in distributed systems. I. Application to telecommunication networks, motivations, and modelling

Privacy protection in recommendation services

Architecture of privacy protection system for recommendation services