On the Complexity of Mining Itemsets from the Crowd Using Taxonomies

We study the problem of frequent itemset mining in domains where data is not recorded in a conventional database but only exists in human knowledge. We provide examples of such scenarios, and present a crowdsourcing model for them. The model uses the crowd as an oracle to find out whether an itemset is frequent or not, and relies on a known taxonomy of the item domain to guide the search for frequent itemsets. In the spirit of data mining with oracles, we analyze the complexity of this problem in terms of (i) crowd complexity, that measures the number of crowd questions required to identify the frequent itemsets; and (ii) computational complexity, that measures the computational effort required to choose the questions. We provide lower and upper complexity bounds in terms of the size and structure of the input taxonomy, as well as the size of a concise description of the output itemsets. We also provide constructive algorithms that achieve the upper bounds, and consider more efficient variants for practical situations.Comment: 18 pages, 2 figures. To be published to ICDT'13. Added missing acknowledgemen

Structurally Tractable Uncertain Data

Many data management applications must deal with data which is uncertain, incomplete, or noisy. However, on existing uncertain data representations, we cannot tractably perform the important query evaluation tasks of determining query possibility, certainty, or probability: these problems are hard on arbitrary uncertain input instances. We thus ask whether we could restrict the structure of uncertain data so as to guarantee the tractability of exact query evaluation. We present our tractability results for tree and tree-like uncertain data, and a vision for probabilistic rule reasoning. We also study uncertainty about order, proposing a suitable representation, and study uncertain data conditioned by additional observations.Comment: 11 pages, 1 figure, 1 table. To appear in SIGMOD/PODS PhD Symposium 201

Uncertainty in Crowd Data Sourcing under Structural Constraints

Applications extracting data from crowdsourcing platforms must deal with the uncertainty of crowd answers in two different ways: first, by deriving estimates of the correct value from the answers; second, by choosing crowd questions whose answers are expected to minimize this uncertainty relative to the overall data collection goal. Such problems are already challenging when we assume that questions are unrelated and answers are independent, but they are even more complicated when we assume that the unknown values follow hard structural constraints (such as monotonicity). In this vision paper, we examine how to formally address this issue with an approach inspired by [Amsterdamer et al., 2013]. We describe a generalized setting where we model constraints as linear inequalities, and use them to guide the choice of crowd questions and the processing of answers. We present the main challenges arising in this setting, and propose directions to solve them.Comment: 8 pages, vision paper. To appear at UnCrowd 201

Privacy Preservation by Disassociation

In this work, we focus on protection against identity disclosure in the publication of sparse multidimensional data. Existing multidimensional anonymization techniquesa) protect the privacy of users either by altering the set of quasi-identifiers of the original data (e.g., by generalization or suppression) or by adding noise (e.g., using differential privacy) and/or (b) assume a clear distinction between sensitive and non-sensitive information and sever the possible linkage. In many real world applications the above techniques are not applicable. For instance, consider web search query logs. Suppressing or generalizing anonymization methods would remove the most valuable information in the dataset: the original query terms. Additionally, web search query logs contain millions of query terms which cannot be categorized as sensitive or non-sensitive since a term may be sensitive for a user and non-sensitive for another. Motivated by this observation, we propose an anonymization technique termed disassociation that preserves the original terms but hides the fact that two or more different terms appear in the same record. We protect the users' privacy by disassociating record terms that participate in identifying combinations. This way the adversary cannot associate with high probability a record with a rare combination of terms. To the best of our knowledge, our proposal is the first to employ such a technique to provide protection against identity disclosure. We propose an anonymization algorithm based on our approach and evaluate its performance on real and synthetic datasets, comparing it against other state-of-the-art methods based on generalization and differential privacy.Comment: VLDB201

Trajectory outlier detection: Algorithms, taxonomies, evaluation, and open challenges

acceptedVersio

Automating Software Customization via Crowdsourcing using Association Rule Mining and Markov Decision Processes

As systems grow in size and complexity so do their configuration possibilities. Users of modern systems are easy to be confused and overwhelmed by the amount of choices they need to make in order to fit their systems to their exact needs. In this thesis, we propose a technique to select what information to elicit from the user so that the system can recommend the maximum number of personalized configuration items. Our method is based on constructing configuration elicitation dialogs through utilizing crowd wisdom. A set of configuration preferences in form of association rules is first mined from a crowd configuration data set. Possible configuration elicitation dialogs are then modeled through a Markov Decision Processes (MDPs). Within the model, association rules are used to automatically infer configuration decisions based on knowledge already elicited earlier in the dialog. This way, an MDP solver can search for elicitation strategies which maximize the expected amount of automated decisions, reducing thereby elicitation effort and increasing user confidence of the result. We conclude by reporting results of a case study in which this method is applied to the privacy configuration of Facebook

Requirements and Use Cases ; Report I on the sub-project Smart Content Enrichment

In this technical report, we present the results of the first milestone phase of the Corporate Smart Content sub-project "Smart Content Enrichment". We present analyses of the state of the art in the fields concerning the three working packages defined in the sub-project, which are aspect-oriented ontology development, complex entity recognition, and semantic event pattern mining. We compare the research approaches related to our three research subjects and outline briefly our future work plan

Sequential Pattern Mining using FCA and Pattern Structures for Analyzing Visitor Trajectories in a Museum

International audienceThis paper presents our work on mining visitor trajectories in Hecht Museum (Haifa, Israel), within the framework of CrossCult Eu-ropean Project about cultural heritage. We present a theoretical and practical research work about the characterization of visitor trajectories and the mining of these trajectories as sequences. The mining process is based on two approaches in the framework of FCA, namely the mining of subsequences without any constraint and the mining of frequent contiguous subsequences. Both approaches are based on pattern structures. In parallel, a similarity measure allows us to build a hierarchical classification which is used for interpretation and characterization of the trajectories w.r.t. four well-known visiting styles