Despite a decade of research on XML keyword search (XKS), demonstration of a high quality XKS system has still eluded the information retrieval community. Existing XKS engines primarily suffer from two limitations. First, although the smallest lowest common ancestor (SLCA) algorithm (or a variant, e.g., ELCA) is widely accepted as a meaningful way to identify subtrees containing the query keywords, SLCA typically performs poorly on documents with missing elements, i.e., (sub)elements that are optional, or appear in some instances of an element type but not all. Second, since keyword search can be ambiguous with multiple possible interpretations, it is desirable for an XKS engine to automatically expand the original query by providing a classification of different possible interpretations of the query w.r.t. the original results. However, existing XKS systems do not support such result-based query expansion. We demonstrate ASTERIX, an innovative XKS engine that addresses these limitations.

We address the problem of debugging programs written for Pregel-like systems. After interviewing Giraph and GPS users, we developed Graft. Graft supports the debugging cycle that users typically go through: (1) Users describe programmatically the set of vertices they are interested in inspecting. During execution, Graft captures the context information of these vertices across supersteps. (2) Using Graft's GUI, users visualize how the values and messages of the captured vertices change from superstep to superstep,narrowing in suspicious vertices and supersteps. (3) Users replay the exact lines of the code vertex.compute() function that executed for the suspicious vertices and supersteps, by copying code that Graft generates into their development environments' line-by-line debuggers. Graft also has features to construct end-to-end tests for Giraph programs. Graft is open-source and fully integrated into Apache Giraph's main code base.

Despite considerable progress in recent years on Tag-based Social Image Retrieval (TagIR), state-of-the-art TagIR systems fail to provide a systematic framework for end users to ask why certain images are not in the result set of a given query and provide an explanation for such missing results. However, as humans, such why-not questions are natural when expected images are missing in the query results returned by a TagIR system. Clearly, it would be very helpful to users if they could pose follow-up why-not questions to seek clarifications on missing images in query results. In this work, we take the first step to systematically answer the why-not questions posed by end-users on TagIR systems. Our answer not only involves the reason why desired images are missing in the results but also suggestion on how the query can be altered so that the user can view these missing images in sufficient number. We present three explanation models, namely result reordering, query relaxation, and query substitution, that enable us to explain a variety of why-not questions. We present an algorithm called WINE (Why-not questIon aNswering Engine) that exploits these models to answer why-not questions efficiently. Experiments on NUS-WIDE dataset demonstrate effectiveness as well as benefits of WINE.

In a previous paper, we laid out the vision of a novel graph query processing paradigm where instead of processing a visual query graph after its construction, it interleaves visual query formulation and processing by exploiting the latency offered by the GUI [4]. Our recent attempts at implementing this vision [4,6], show significant improvement in the system response time (SRT) for subgraph queries. However, these efforts are designed specifically for graph databases containing a large collection of small or medium-sized graphs. Consequently, its frequent fragment-based action-aware indexing schemes and query processing strategy are unsuitable for supporting subgraph queries on large networks containing thousands of nodes and edges. In this demonstration, we present a novel system called QUBLE (QUery Blender for Large nEtworks) to realize this novel paradigm on large networks. We demonstrate various innovative features of QUBLE and its promising performance.

Keyword search for smallest lowest common ancestors (SLCAs) in XML data has been widely accepted as a meaningful way to identify matching nodes where their subtrees contain an input set of keywords. Although SLCA and its variants (e.g.,MLCA) perform admirably in identifying matching nodes, surprisingly, they perform poorly for searches on irregular schemas that have missing elements, that is, (sub)elements that are optional, or appear in some instances of an element type but not all (e.g., a "population" subelement in a "city" element might be optional, appearing when the population is known and absent when the population is unknown). In this paper, we generalize the SLCA search paradigm to support queries involving missing elements. Specifically, we propose a novel property called optionality resilience that specifies the desired behaviors of an XML keyword search (XKS) approach for queries involving missing elements. We present two variants of a novel algorithm called MESSIAH (Missing Element-conSciouS hIgh-quality SLCA searcH), which are optionality resilient to irregular documents. MESSIAH logically transforms an XML document to a minimal full document where all missing elements are represented as empty elements, i.e., the irregular schema is made "regular", and then employs efficient strategies to identify partial and complete full SLCA nodes (SLCA nodes in the full document) from it. Specifically, it generates the same SLCA nodes as any state-of-the-art approach when the query does not involve missing elements but avoids irrelevant results when missing elements are involved. Our experimental study demonstrates the ability of MESSIAH to produce superior quality search results.

Tags associated with social images are valuable information source for superior tag-based image retrieval (TagIR) experiences. One of the key issues in TagIR is to learn the effectiveness of a tag in describing the visual content of its annotated image, also known as tag relevance. One of the most effective approaches in the literature for tag relevance learning is neighbor voting. In this approach a tag is considered more relevant to its annotated image (also known as the seed image) if the tag is also used to annotate the neighbor images (nearest neighbors by visual similarity). However, the state-of-the-art approach that realizes the neighbor voting scheme does not explore the possibility of exploiting the content (e.g., degree of visual similarity between the seed and neighbor images) and contextual (e.g., tag association by co-occurrence) features of social images to further boost the accuracy of TagIR. In this paper, we identify and explore the viability of four content and context-based dimensions namely, image similarity, tag matching, tag influence, and refined tag relevance, in the context of tag relevance learning for TagIR. With alternative formulations under each dimension, this paper empirically evaluated 20 neighbor voting schemes with 81 single-tag queries on nus-wide dataset. Despite the potential benefits that the contextual information related to tags bring in to image search, surprisingly, our experimental results reveal that the content-based (image similarity) dimension is still the king as it significantly improves the accuracy of tag relevance learning for TagIR. On the other hand, tag relevance learning does not benefit from the context-based dimensions in the voting schemes.