Abolfazl Asudeh

A general model for MAC protocol selection in wireless sensor networks

Wireless sensor networks (WSNs) have become relatively common in recent years with application scenarios ranging from low-traffic soil condition sensing to high-traffic video surveillance networks. Each of these applications has its own specific structure, goals, and requirements. Medium access control (MAC) protocols play a significant role in WSNs and should be tuned to the particular application. However, there is no general model that can aid in the selection and tuning of MAC protocols for different applications, imposing a heavy burden on the design engineers of these networks. Having a precise analytical model for each MAC protocol, on the other hand, is almost impossible. Using the intuition that protocols in the same behavioral set perform similarly, our goal in this paper is to introduce a general model that can help select the protocol(s) that satisfy given requirements from a protocol set that performs best for a given context. We define the Combined Performance Function (CPF) to demonstrate the performance of different category protocols for different contexts. Having developed the general model, we then discuss the models scalability in terms of adding new protocols, categories, requirements, and performance criteria. Considering energy consumption and delay as the initial performance criteria of the model, we focus on deriving mathematical models for them. Previous rules of thumb for selecting MAC protocols support the results extracted from CPF, providing a practical verification for our model. We further validate our models with the help of simulation studies.

Query reranking as a service

The ranked retrieval model has rapidly become the de facto way for search query processing in client-server databases, especially those on the web. Despite of the extensive efforts in the database community on designing better ranking functions/mechanisms, many such databases in practice still fail to address the diverse and sometimes contradicting preferences of users on tuple ranking, perhaps (at least partially) due to the lack of expertise and/or motivation for the database owner to design truly effective ranking functions. This paper takes a different route on addressing the issue by defining a novel query reranking problem, i.e., we aim to design a third-party service that uses nothing but the public search interface of a client-server database to enable the on-the-fly processing of queries with any user-specified ranking functions (with or without selection conditions), no matter if the ranking function is supported by the database or not. We analyze the worst-case complexity of the problem and introduce a number of ideas, e.g., on-the-fly indexing, domination detection and virtual tuple pruning, to reduce the average-case cost of the query reranking algorithm. We also present extensive experimental results on real-world datasets, in both offline and live online systems, that demonstrate the effectiveness of our proposed techniques.

Efficient Computation of Regret-ratio Minimizing Set: A Compact Maxima Representative

Finding the maxima of a database based on a user preference, especially when the ranking function is a linear combination of the attributes, has been the subject of recent research. A critical observation is that the em convex hull is the subset of tuples that can be used to find the maxima of any linear function. However, in real world applications the convex hull can be a significant portion of the database, and thus its performance is greatly reduced. Thus, computing a subset limited to

MobiFace: A Mobile Application for Faceted Search over Hidden Web Databases

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Efficient Computation of Subspace Skyline over Categorical Domains

tuples that minimizes the regret ratio (a measure of the users dissatisfaction with the result from the limited set versus the one from the entire database) is of interest. In this paper, we make several fundamental theoretical as well as practical advances in developing such a compact set. In the case of two dimensional databases, we develop an optimal linearithmic time algorithm by leveraging the ordering of skyline tuples. In the case of higher dimensions, the problem is known to be NPcomplete. As one of our main results of this paper, we develop an approximation algorithm that runs in linearithmic time and guarantees a regret ratio, within any arbitrarily small user-controllable distance from the optimal regret ratio. The comprehensive set of experiments on both synthetic and publicly available real datasets confirm the efficiency, quality of output, and scalability of our proposed algorithms.

Leveraging similarity joins for signal reconstruction

A large number of web databases are hidden behind form-based interfaces (consisting of interface components such as textboxes, drop-down boxes, etc) for users to enter their desired values in a search query. Form-based interfaces are not always easy to use and error-prone on mobile devices, mainly because of limitations such as smaller screen, trickier text input, etc. We build MobiFace which is a faceted search system over a hidden database for mobile users. The goal of our system is to help a user to find her tuple of interest with as fewer questions as possible. MobiFace dynamically suggests facets for drilling down into the hidden web database such that the navigation cost is minimized. We run the experiment over real IMDB dataset on a mobile device.

Efficient construction of approximate ad-hoc ML models through materialization and reuse

Platforms such as AirBnB, Zillow, Yelp, and related sites have transformed the way we search for accommodation, restaurants, etc. The underlying datasets in such applications have numerous attributes that are mostly Boolean or Categorical. Discovering the skyline of such datasets over a subset of attributes would identify entries that stand out while enabling numerous applications. There are only a few algorithms designed to compute the skyline over categorical attributes, yet are applicable only when the number of attributes is small. In this paper, we place the problem of skyline discovery over categorical attributes into perspective and design efficient algorithms for two cases. (i) In the absence of indices, we propose two algorithms, ST-S and ST-P, that exploit the categorical characteristics of the datasets, organizing tuples in a tree data structure, supporting efficient dominance tests over the candidate set. (ii) We then consider the existence of widely used precomputed sorted lists. After discussing several approaches, and studying their limitations, we propose TA-SKY, a novel threshold style algorithm that utilizes sorted lists. Moreover, we further optimize TA-SKY and explore its progressive nature, making it suitable for applications with strict interactive requirements. In addition to the extensive theoretical analysis of the proposed algorithms, we conduct a comprehensive experimental evaluation of the combination of real (including the entire AirBnB data collection) and synthetic datasets to study the practicality of the proposed algorithms. The results showcase the superior performance of our techniques, outperforming applicable approaches by orders of magnitude.

Discovering the skyline of web databases

Signal reconstruction problem (SRP) is an important optimization problem where the objective is to identify a solution to an underdetermined system of linear equations that is closest to a given prior. It has a substantial number of applications in diverse areas including network traffic engineering, medical image reconstruction, acoustics, astronomy and many more. Most common approaches for SRP do not scale to large problem sizes. In this paper, we propose a dual formulation of this problem and show how adapting database techniques developed for scalable similarity joins provides a significant speedup. Extensive experiments on real-world and synthetic data show that our approach produces a significant speedup of up to 20x over competing approaches. PVLDB Reference Format: Abolfazl Asudeh, Azade Nazi, Jees Augustine, Saravanan Thirumuruganathan, Nan Zhang, Gautam Das, and Divesh Srivastava. Leveraging Similarity Joins for Signal Reconstruction. PVLDB, 11 (10): 1276-1288, 2018. DOI: https://doi.org/10.14778/3231751.3231752

Crowdsourcing Pareto-Optimal Object Finding By Pairwise Comparisons

Machine learning has become an essential toolkit for complex analytic processing. Data is typically stored in large data warehouses with multiple dimension hierarchies. Often, data used for building an ML model are aligned on OLAP hierarchies such as location or time. In this paper, we investigate the feasibility of efficiently constructing approximate ML models for new queries from previously constructed ML models by leveraging the concepts of model materialization and reuse. For example, is it possible to construct an approximate ML model for data from the year 2017 if one already has ML models for each of its quarters? We propose algorithms that can support a wide variety of ML models such as generalized linear models for classification along with K-Means and Gaussian Mixture models for clustering. We propose a cost based optimization framework that identifies appropriate ML models to combine at query time and conduct extensive experiments on real-world and synthetic datasets. Our results indicate that our framework can support analytic queries on ML models, with superior performance, achieving dramatic speedups of several orders in magnitude on very large datasets. PVLDB Reference Format: Sona Hasani, Saravanan Thirumuruganathan, Abolfazl Asudeh, Nick Koudas and Gautam Das. Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse. PVLDB, 11 (11): 1468-1481, 2018. DOI: https://doi.org/10.14778/3236187.3236199