Omer Barkol

Web Interface Interpretation Using Graph Grammars

With the advent of the Internet, it is desirable to interpret and extract useful information from the Web. One major challenge in Web interface interpretation is to discover the semantic structure underlying a Web interface. Many heuristic approaches have been developed to discover and group semantically related interface objects. However, those approaches cannot solve the problem of nonuniformity satisfactorily and are not able to tag the semantic role of each object. Distinct from existing approaches, this paper develops a robust and formal approach to recovering interface semantics using graph grammars. Because of the distinct capability of spatial specifications in the abstract syntax, the spatial graph grammar (SGG) is selected to perform the semantic grouping and interpretation of segmented screen objects. Instead of analyzing HTML source codes, we apply an efficient image-processing technology to recognize atomic interface objects from the screenshot of an interface and produce a spatial graph, which records significant spatial relations among recognized objects. A spatial graph is more concise than its corresponding document object model structure and, thus, facilitates interface analysis and interpretation. Based on the spatial graph, the SGG parser recovers the hierarchical relations among interface objects.

Approximate graph mining with label costs

Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to define a cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that approximate mining yields interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.

Graph mining for discovering infrastructure patterns in configuration management databases

A configuration management database (CMDB) can be considered to be a large graph representing the IT infrastructure entities and their interrelationships. Mining such graphs is challenging because they are large, complex, and multi-attributed and have many repeated labels. These characteristics pose challenges for graph mining algorithms, due to the increased cost of subgraph isomorphism (for support counting) and graph isomorphism (for eliminating duplicate patterns). The notion of pattern frequency or support is also more challenging in a single graph, since it has to be defined in terms of the number of its (potentially, exponentially many) embeddings. We present CMDB-Miner, a novel two-step method for mining infrastructure patterns from CMDB graphs. It first samples the set of maximal frequent patterns and then clusters them to extract the representative infrastructure patterns. We demonstrate the effectiveness of CMDB-Miner on real-world CMDB graphs, as well as synthetic graphs.

On d -Multiplicative Secret Sharing

A multiplicative secret sharing scheme allows players to multiply two secret-shared field elements by locally converting their shares of the two secrets into an additive sharing of their product. Multiplicative secret sharing serves as a central building block in protocols for secure multiparty computation (MPC). Motivated by open problems in the area of MPC, we introduce the more general notion of d-multiplicative secret sharing, allowing to locally multiply d shared secrets, and study the type of access structures for which such secret sharing schemes exist.While it is easy to show that d-multiplicative schemes exist if no d unauthorized sets of players cover the whole set of players, the converse direction is less obvious for d≥3. Our main result is a proof of this converse direction, namely that d-multiplicative schemes do not exist if the set of players is covered by d unauthorized sets. In particular, t-private d-multiplicative secret sharing among k players is possible if and only ifk>dt.Our negative result holds for arbitrary (possibly inefficient or even nonlinear) secret sharing schemes and implies a limitation on the usefulness of secret sharing in the context of MPC. Its proof relies on a quantitative argument inspired by communication complexity lower bounds.

A robust similarity measure for automatic inspection

We introduce a new similarity measure that is insensitive to sub-pixel misregistration. The proposed measure is essential in some differences detection scenarios. For example, in a setting where a digital reference is compared to an image, where the imaging process introduces deformations that appear as non constant misregistration between the two images. Our goal is to ignore image differences that result from misregistration and detect only the true, albeit minute, defects. In order to define a misregistration insensitive similarity, we argue that a similarity measure must respect convex combinations. We show that the well known SSIM [1] does not hold this property and propose a modified version of SSIM that respects convex combinations. We then use this measure to define Sub-Pixel misregistration aware SSIM (SPSSIM).

Multi-source uncertain entity resolution

In this work we present a multi-source uncertain entity resolution model and show its implementation in a use case of Yad Vashem, the central repository of Holocaust-era information. The Yad Vashem dataset is unique with respect to classic entity resolution, by virtue of being both massively multi-source and by requiring multi-level entity resolution. With todays abundance of information sources, this project motivates the use of multi-source resolution on a big-data scale. We instantiate the proposed model using the MFIBlocks entity resolution algorithm and a machine learning approach, based upon decision trees to transform soft clusters into ranked clustering of records, representing possible entities. An extensive empirical evaluation demonstrates the unique properties of this dataset that make it a good candidate for multi-source entity resolution. We conclude with proposing avenues for future research in this realm. HighlightsUncertain Entity Resolution allows creating multiple narratives from complementary sources of data.The approach was demonstrated during a unique project performed on the Yad Vashem Names database.Algorithms implementing the approach were empirically evaluated on a tagged subset on various configurations and versus equivalent algorithms.The accurate and insightful results are being integrated into Yad Vashem systems and user applications.

Infrastructure Pattern Discovery in Configuration Management Databases via Large Sparse Graph Mining

A configuration management database (CMDB) can be considered to be a large graph representing the IT infrastructure entities and their inter-relationships. Mining such graphs is challenging because they are large, complex, and multi-attributed, and have many repeated labels. These characteristics pose challenges for graph mining algorithms, due to the increased cost of sub graph isomorphism (for support counting), and graph isomorphism (for eliminating duplicate patterns). The notion of pattern frequency or support is also more challenging in a single graph, since it has to be defined in terms of the number of its (potentially, exponentially many) embeddings. We present CMDB-Miner, a novel two-step method for mining infrastructure patterns from CMDB graphs. It first samples the set of maximal frequent patterns, and then clusters them to extract the representative infrastructure patterns. We demonstrate the effectiveness of CMDB-Miner on real-world CMDB graphs.

Automatic Policy Rule Extraction for Configuration Management

We propose a new IT automation technology for configuration management: automatic baseline policy extraction out of the Configuration Management Data Base (CMDB). Whereas authoring a configuration policy rule manually is time consuming and unlikely to realize the actual state of the configurations in the overall organization, this new approach summarizes the de-facto configurations from the data. IT staff, instead of authoring the policy rule, is required to simply validate and possibly enhance the automatically extracted policy. Our technology applies data-mining to organizations configuration assets in the CMDB, and automatically identifies repeating structures of compound configurations. Based on these repeating structures, we build policy rules for compound configuration items. The heart of our technique is a new distance measure we introduce between the configuration assets, whose computation is reduced to a minimum-cost flow problem.

Multi-Source Uncertain Entity Resolution at Yad Vashem: Transforming Holocaust Victim Reports into People

In this work we describe an entity resolution project performed at Yad Vashem, the central repository of Holocaust-era information. The Yad Vashem dataset is unique with respect to classic entity resolution, by virtue of being both massively multi-source and by requiring multi-level entity resolution. With todays abundance of information sources, this project sets an example for multi-source resolution on a big-data scale. We discuss a set of requirements that led us to choose the MFIBlocks entity resolution algorithm in achieving the goals of the application. We also provide a machine learning approach, based upon decision trees to transform soft clusters into ranked clustering of records, representing possible entities. An extensive empirical evaluation demonstrates the unique properties of this dataset, highlighting the shortcomings of current methods and proposing avenues for future research in this realm.

Visibly Pushdown Languages for a GUI Parsing Application with Probabilistic Lexer

Automatic understanding of GUI (Graphic User Interfaces) is vitally important for applications such as quality assurance, user monitoring, speech activated devices, automatic generation of GUI for application accessibility, and GUI design. Likewise, automatic understanding of visually structured documents (e.g. PDF files) is vitally important for data mining purposes. Current GUI parsers share two major shortcomings: First, instead of representing the user experience, they are tightly coupled to the underlying object model of the GUI. Second, from a linguistic point of view, they are either too restrictive to describe enough GUIs, or too permissive, in which case, the language structure itself becomes very fragile. We designed and implemented a new GUI parsing language which avoids these problems. It is easy to maintain, robust to changes in the input, and finally - as a computer program - decidable and fast to parse.