arXiv subjects

Yodsawalai Chodpathumwan

Publications and source records attributed to Yodsawalai Chodpathumwan.

3 recordsLinked to original sources

arXiv2021

Structural Generalizability: The Case of Similarity Search

Graph similarity search algorithms usually leverage the structural properties of a database. Hence, these algorithms are effective only on some structural variations of the data and are ineffective on other forms, which makes them hard to use. Ideally, one would like to design a data analytics algorithm that is structurally robust, i.e., it returns essentially the same accurate results over all possible structural variations of a dataset. We propose a novel approach to create a structurally robust similarity search algorithm over graph databases. We leverage the classic insight in the database literature that schematic variations are caused by having constraints in the database. We then present RelSim algorithm which is provably structurally robust under these variations. Our empirical studies show that our proposed algorithms are structurally robust while being efficient and as effective as or more effective than the state-of-the-art similarity search algorithms.

cs.DB↗

arXiv2018

Cost-Effective Conceptual Design Using Taxonomies

It is known that annotating named entities in unstructured and semi-structured data sets by their concepts improves the effectiveness of answering queries over these data sets. As every enterprise has a limited budget of time or computational resources, it has to annotate a subset of concepts in a given domain whose costs of annotation do not exceed the budget. We call such a subset of concepts a {\it conceptual design} for the annotated data set. We focus on finding a conceptual design that provides the most effective answers to queries over the annotated data set, i.e., a {\it cost-effective conceptual design}. Since, it is often less time-consuming and costly to annotate general concepts than specific concepts, we use information on superclass/subclass relationships between concepts in taxonomies to find a cost-effective conceptual design. We quantify the amount by which a conceptual design with concepts from a taxonomy improves the effectiveness of answering queries over an annotated data set. If the taxonomy is a tree, we prove that the problem is NP-hard and propose an efficient approximation and pseudo-polynomial time algorithms for the problem. We further prove that if the taxonomy is a directed acyclic graph, given some generally accepted hypothesis, it is not possible to find any approximation algorithm with reasonably small approximation ratio for the problem. Our empirical study using real-world data sets, taxonomies, and query workloads shows that our framework effectively quantifies the amount by which a conceptual design improves the effectiveness of answering queries. It also indicates that our algorithms are efficient for a design-time task with pseudo-polynomial algorithm being generally more effective than the approximation algorithm.

cs.DB↗

arXiv2014

Representation Independent Analytics Over Structured Data

Database analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be represented using many different structures and the structural properties observed over particular representations do not necessarily hold for alternative structures. Thus, there is no guarantee that current database analytics algorithms will still provide the correct insights, no matter what structures are chosen to organize the database. Because these algorithms tend to be highly effective over some choices of structure, such as that of the databases used to validate them, but not so effective with others, database analytics has largely remained the province of experts who can find the desired forms for these algorithms. We argue that in order to make database analytics usable, we should use or develop algorithms that are effective over a wide range of choices of structural organizations. We introduce the notion of representation independence, study its fundamental properties for a wide range of data analytics algorithms, and empirically analyze the amount of representation independence of some popular database analytics algorithms. Our results indicate that most algorithms are not generally representation independent and find the characteristics of more representation independent heuristics under certain representational shifts.

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