The central concept of browser fingerprinting is the collection of device-specific information for identification or security purposes. This chapter provides an overview of the research conducted in the field of browser fingerprinting and presents an entry point for newcomers. Relevant literature is examined to understand the current research in the field of browser fingerprinting. Both research in the field of crafting browser fingerprints and protection against it is included. Finally, current research challenges and future research directions are presented and discussed.

Abstract Tsetlin machines (TMs) are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In addition to being natively interpretable, they have provided competitive accuracy for various tasks. In this paper, we increase the computing power of TMs by proposing a first-order logic-based framework with Herbrand semantics. The resulting TM isrelationaland can take advantage of logical structures appearing in natural language, to learn rules that represent how actions and consequences are related in the real world. The outcome is a logic program of Horn clauses, bringing in a structured view of unstructured data. In closed-domain question-answering, the first-order representation produces 10 × more compact KBs, along with an increase in answering accuracy from 94.83%to 99.48%. The approach is further robust towards erroneous, missing, and superfluous information, distilling the aspects of a text that are important for real-world understanding

We address the challenge of reconstructing historical counts from aggregated, possibly overlapping historical reports. For example, given the monthly and weekly sums, how can we find the daily counts of people infected with flu? We propose an approach, called ARES (Automatic REStoration), that performs automatic data reconstruction in two phases: (1) first, it estimates the sequence of historical counts utilizing domain knowledge, such as smoothness and periodicity of historical events; (2) then, it uses the estimated sequence to learn notable patterns in the target sequence to refine the reconstructed time series. In order to derive such patterns, ARES uses an annihilating filter technique. The idea is to learn a linear shift-invariant operator whose response to the desired sequence is (approximately) zero-yielding a set of null-space equations that the desired signal should satisfy, without the need for the accompanying data. The reconstruction accuracy can be further improved by applying the second phase iteratively. We evaluate ARES on the real epidemiological data from the Tycho project and demonstrate that ARES recovers historical data from aggregated reports with high accuracy. In particular, it considerably outperforms top competitors, including least squares approximation and the more advanced H-FUSE method (42% and 34% improvement based on average RMSE, respectively).

Recovering a time sequence of events from multiple aggregated and possibly overlapping reports is a major challenge in historical data fusion. The goal is to reconstruct a higher resolution event sequence from a mixture of lower resolution samples as accurately as possible. For example, we may aim to disaggregate overlapping monthly counts of people infected with measles into weekly counts. In this paper, we propose a novel data disaggregation method, called H ome R un , that exploits an alternative representation of the sequence and finds the spectrum of the target sequence. More specifically, we formulate the problem as so-called basis pursuit using the Discrete Cosine Transform (DCT) as a sparsifying dictionary and impose non-negativity and smoothness constraints. H ome R un utilizes the energy compaction feature of the DCT by finding the sparsest spectral representation of the target sequence that contains the largest (most important) coefficients. We leverage the Alternating Direction Method of Multipliers to solve the resulting optimization problem with scalable and memory efficient steps. Experiments using real epidemiological data show that our method considerably outperforms the state-of-the-art techniques, especially when the DCT of the sequence has a high degree of energy compaction.

In this paper, we address the challenge of recovering a time sequence of counts from aggregated historical data. For example, given a mixture of the monthly and weekly sums, how can we find the daily counts of people infected with flu? In general, what is the best way to recover historical counts from aggregated, possibly overlapping historical reports, in the presence of missing values? Equally importantly, how much should we trust this reconstruction? We propose H-Fuse, a novel method that solves above problems by allowing injection of domain knowledge in a principled way, and turning the task into a well-defined optimization problem. H-Fuse has the following desirable properties: (a) Effectiveness, recovering historical data from aggregated reports with high accuracy; (b) Self-awareness, providing an assessment of when the recovery is not reliable; (c) Scalability, computationally linear on the size of the input data. Experiments on the real data (epidemiology counts from the Tycho project [13]) demonstrates that H-FUSE reconstructs the original data 30 – 81% better than the least squares method.

In this seminar we will consider how concepts of information fusion, crowdsourcing and mobility complement each other and accelerate novel advanced research directions in mobile data management. We will elaborate on each of those concepts and explore their synergy under a prominent scenario of situation assessment in multi-robot search and rescue missions.

In this paper, we introduce an automatic information fusion method that exploits the collective intelligence of mobile robots to efficiently “crowdsource” victim detection tasks. We reduce the load on the operators requiring them to acknowledge only presence of the victim in an image (to annotate the image). The task of finding victim location is performed via automatic fusion of annotated images from the image queue.

In this work, we present a set of techniques that considerably improve the performance of executing concurrent MapReduce jobs. Our proposed solution relies on proper resource allocation for concurrent Hive jobs based on data dependency, inter-query optimization and modeling of Hadoop cluster load. To the best of our knowledge, this is the first work towards Hive/MapReduce job optimization which takes Hadoop cluster load into consideration. We perform an experimental study that demonstrates 233% reduction in execution time for concurrent vs sequential execution schema. We report up to 40% extra reduction in execution time for concurrent job execution after resource usage optimization. The results reported in this paper were obtained in a pilot project to assess the feasibility of migrating A/B testing from Teradata + SAS analytics infrastructure to Hadoop. This work was performed on eBay production Hadoop cluster.

A major challenge in large-scale process monitoring is to recognize significant transitions in the process conditions and to distinguish them from random fluctuations that do not produce a notable change in the process dynamics. Such transitions should be recognized at the early stages of their development using a minimal "snapshot" of the observable process log. We developed a novel approach to detect notable transitions based on analysis of coherent behavior of frequency components in the process log (coherency portraits). We have found that notable transitions in the process dynamics are characterized by unique coherency portraits, which are also invariant with respect to random process fluctuations. Our experimental study demonstrates significant efficiency of our approach as compared to traditional change detection techniques.

Unattended Wireless Sensor Networks (UWSNs) are characterized by long periods of disconnected operation and fixed or irregular intervals between visits by the sink. The absence of an online trusted third party, i.e., an on-site sink, makes existing trust management schemes used in legacy wireless sensor networks not applicable to UWSNs directly. In this paper, we propose a trust management scheme for UWSNs to provide efficient, robust and scalable trust data storage. For trust data storage, we employ geographic hash table to efficiently identify data storage nodes and to significantly reduce storage cost. We demonstrate, through detailed analyses and extensive simulations, that the proposed scheme is efficient, robust, and scalable.

The combined effect of various problems such as congestion, collisions and route unavailability for data in Data-Intensive Sensor Networks is hard to estimate. This is one of the major reasons why existing solutions that try to optimize all these do not scale and have limited applicability. Our light-weight approach uses decisions made locally by individual sensors to efficiently discover desirable information processing patterns. Since, each node makes decisions based on local knowledge, this approach is more scalable.

We introduce a novel strategy for data processing in Wireless Sensor Networks (WSNs) in the case of emergency fire evacuation with stringent delay constraints. Such networks should perform distributed emergency assessment, continuous emergency monitoring, and dynamic selection of optimal evacuation strategies. The obvious complexity of these tasks restricts applying existing WSN optimization solutions. Our approach is based on considering the WSN as a complex adaptive system where a complicated task of multi-agent scheduling is factored into a set of smaller subtasks. In this case, decisions made locally by individual sensors can efficiently converge into desirable information processing patterns. A notable feature of our method is its scalability, which allows the sensornet to operate with sufficient quality of service under heavy information loads. We demonstrate the utility of our approach using different fire evacuation scenarios.

We introduce a distributed grid-based scheduling access scheme that mitigates high data loss in data intensive sensor networks. Our approach alleviates transmission collisions by applying virtual grids and adopting Latin Squares Characteristic to time slot assignments. We demonstrate that our technique efficiently handles sensor mobility with acceptable data loss and low overhead.

A WAN environment, such as the Internet, connects a federation of hundreds of servers with tens of thousands of clients, which poses a substantial scalability challenge. Clients may choose among sources that vary in both their content and quality as well as in their access latencies. At the same time, Internet accessible data sources exhibit transient behavior; the unpredictable behavior of a dynamic WAN results in a wide variability in access cost (end-to-end latency). This motivates a need for a source selection strategy that requires maintaining access cost distributions (latency profiles) for each client/server pair. However, in the presence of hundreds of servers and thousands of clients, managing latency profiles cannot scale. We present a scalable methodology to manage latency profiles that use non-random associations between client/server pairs. Such non-random associations may be identified by topology-independent measures such as correlation and mutual information. We propose a Catalog infrastructure that implements our methodology and utilize non-randomly associated latency profiles to estimate access cost distribution for client/server pairs. We perform an extensive experimental study demonstrating feasibility and efficiency of our approach.

Sensor Networks have brought closer than ever before the network and database research communities. A novel sensor data management paradigm appeared with the development of data centric routing protocols viewing the network as a huge distributed database. However, this paradigm is typically supported through a query processing layer that treats the wireless network as a black box and underestimates its fundamental limitations. In this seminar we elaborate on these limitations and utilize them in favor of efficient data management in wireless sensor environments.

Data Intensive Mobile Sensor Networks (DIMSNs) introduce a promising but still under-utilized technology. Meanwhile, there is a growing confidence that certain applications (Killer Apps) have a potential to create a sustained market for this technology. For example, a large team of cooperative mobile robots can be considered as a wireless sensornet composed of a number of mobile nodes most of which are powerconstrained. Such mobile robots can be deployed in conjunction with stationary sensor nodes to acquire and process data for surveillance and tracking, environmental monitoring for highly sensitive areas, or execute search and rescue operations. This example illustrates conceptual attractiveness of the DIMSN systems that generates interesting and appealing research challenges (e.g., intelligent mobile agents, semantically enriched and contextaware wireless services, smart network monitoring infrastructures). However, while providing excellent funding opportunities, those challenges often underestimate the GRAND DETERRENTS that make moves towards practical data-intensive mobile sensornets extremely difficult.

In this paper we propose a query-driven approach for tuning the time/energy trade-off in sensor networks with mobile sensors. The tuning factors include re-positioning of mobile sensors and changing their transmission ranges. We propose an algebraic query optimization framework that explores these factors while utilizing collision-free concurrent data transmissions with different degrees of data filtering and aggregation.

We consider an architecture of mediators and wrappers for Internet accessible WebSources of limited query capability. Each call to a source is a WebSource Implementation (WSI) and it is associated with both a capability and (a possibly dynamic) cost. The multiplicity of WSIs with varying costs and capabilities increases the complexity of a traditional optimizer that must assign WSIs for each remote relation in the query while generating an (optimal) plan. We present a two-phase Web Query Optimizer (WQO). In a pre-optimization phase, the WQO selects one or more WSIs for a pre-plan; a pre-plan represents a space of query evaluation plans (plans) based on this choice of WSIs. The WQO uses cost-based heuristics to evaluate the choice of WSI assignment in the pre-plan and to choose a good pre-plan. The WQO uses the pre-plan to drive the extended relational optimizer to obtain the best plan for a pre-plan. A prototype of the WQO has been developed. We compare the effectiveness of the WQO, i.e., its ability to efficiently search a large space of plans and obtain a low cost plan, in comparison to a traditional optimizer. We also validate the cost-based heuristics by experimental evaluation of queries in the noisy Internet environment.

We demonstrate a Web Query Optimizer (WQO) within an architecture of mediators and wrappers, for WebSources of limited capability in a wide area environment. The WQO has several innovative features, including a CBR (capability based rewriting) tool, an enhanced randomized relational optimizer extended to a Web environment, and a WebWrapper cost model that can provide relevant metrics for accessing WebSources. The prototype has been tested against a number of WebSources.