In this paper, we study the problem of numerical multi-table question answering (MTQA) over large-scale table collections (e.g., online data repositories). This task is essential in many analytical applications. Existing MTQA solutions, such as text-to-SQL or open-domain MTQA methods, are designed for databases and struggle when applied to large-scale table collections. The key limitations include: (1) Limited support for complex table relationships; (2) Ineffective retrieval of relevant tables at scale; (3) Inaccurate answer generation. To overcome these limitations, we propose DMRAL, a Decomposition-driven Multi-table Retrieval and Answering framework for MTQA over large-scale table collections, which consists of: (1) constructing a table relationship graph to capture complex relationships among tables; (2) Table-Aligned Question Decomposer and Coverage-Aware Retriever, which jointly enable the effective identification of relevant tables from large-scale corpora by enhancing the question decomposition quality and maximizing the question coverage of retrieved tables; and (3) Sub-question Guided Reasoner, which produces correct answers by progressively generating and refining the reasoning program based on sub-questions. Experiments on two MTQA datasets demonstrate that DMRAL significantly outperforms existing state-of-the-art MTQA methods, with an average improvement of 24% in table retrieval and 55% in answer accuracy.

Podcasts have continued to grow in popularity over the last two decades, with more than 4.52 million podcasts and 584 million listeners across the globe in 2025. Developing effective search systems for web-scale podcast corpora is of vital importance. Previous research has approached the search task primarily through text representation using a single transcription of the audio content via automatic speech recognition (ASR) models. However, there is currently limited understanding about how variation in podcast representations influences ranking, retrieval, and relevance assessment.

Abstract Missing values in tabular data lakes can severely impact data analysis and diminish the performance in downstream applications. We highlight that a robust imputation strategy should properly take three aspects of variety into consideration: source of imputed value, the types of tables involved, and the data types of the missing value. Existing imputation methods rely on estimation-based approaches (using a model trained on data from the same table to estimate missing values) or search-based approaches (retrieving values from other tables). Unfortunately, none of these approaches effectively incorporate all three aspects of variety. To address this gap, we propose , a novel framework that uses a C ombination of E stimation-based and S earch-based methods for missing value I mputation in D ata lakes. contains three core modules: (1) the , which efficiently discovers candidate values from tables by exploiting the contextual information; (2) the , which introduces an influence function and a sampling-based exploration strategy to yield accurate estimated values; (3) the , which determines the most suitable method based on table-level and column-level statistics. Extensive experiments conducted on three data lakes demonstrate that effectively and efficiently addresses the missing value problem.

Line charts are a valuable tool for data analysis and exploration, distilling essential insights from a dataset. However, access to the underlying data used to create a line chart is rarely readily available. In this paper, we explore a novel dataset discovery problem, dataset discovery via line charts, focusing on the use of line charts as queries to discover datasets within a large data repository that are capable of generating similar line charts. To solve this problem, we propose a novel approach called Fine-grained Cross-modal Relevance Learning Model (FCM), which aims to estimate the relevance between a line chart and raw data from a candidate dataset. To achieve this goal, FCM first applies a visual element extractor to extract visual elements, i.e., lines and y-axis ticks, from a line chart. Then, two novel segment-level encoders are applied to learn representations for a line chart and a candidate dataset, preserving fine-grained information, followed by a cross-modal matcher that matchs the learned representations in a fine-grained manner. Furthermore, we extend FCM to support line chart query generated based on data aggregation. Last, we provide a benchmark tailored for this problem since no such dataset exists. Extensive evaluation on the new benchmark verifies the effectiveness of our proposed method. Specifically, our proposed approach surpasses the best baseline by 30.1% and 41.0% in terms of prec@50 and ndcg@50, respectively.

This work proposes Hyperdimensional Computing (HDC) as a design paradigm [13] to facilitate search and recommendation activities for disabled users employing symbolic augmentative and alternative communication (AAC) systems. Such a context necessitates flexibility and composability in item and query representations as a consequence of vocabularies being tailored to an individual user. HDC is suggested to meet these needs in an efficient manner. However, construction and empirical evaluation are left to additional research.

Social media platforms must filter sexist content in compliance with governmental regulations. Current machine learning approaches can reliably detect sexism based on standardized definitions, but often neglect the subjective nature of sexist language and fail to consider individual users' perspectives. To address this gap, we adopt a perspectivist approach, retaining diverse annotations rather than enforcing gold-standard labels or their aggregations, allowing models to account for personal or group-specific views of sexism. Using demographic data from Twitter, we employ large language models (LLMs) to personalize the identification of sexism.

In this paper, given a user's query set and budget, we aim to use the limited budget to help users assemble a set of datasets that can enrich a base dataset by introducing the maximum number of distinct tuples (i.e., maximizing distinctiveness). We prove this problem to be NP-hard. A greedy algorithm using exact distinctiveness computation attains an approximation ratio of (1-e-1 )/2, but it lacks efficiency and scalability due to its frequent computation of the exact distinctiveness marginal gain of any candidate dataset for selection. This requires scanning through every tuple in candidate datasets and thus is unaffordable in practice. To overcome this limitation, we propose an efficient machine learning (ML)-based method for estimating the distinctiveness marginal gain of any candidate dataset. This effectively eliminates the need to test each tuple individually. Estimating the distinctiveness marginal gain of a dataset involves estimating the number of distinct tuples in the tuple sets returned by each query in a query set across multiple datasets. This can be viewed as the cardinality estimation for a query set on a set of datasets, and the proposed method is the first to tackle this cardinality estimation problem. This is a significant advancement over prior methods that were limited to single-query cardinality estimation on a single dataset and struggled with identifying overlaps among tuple sets returned by each query in a query set across multiple datasets. Extensive experiments using five real-world data pools demonstrate that our algorithm, which utilizes ML-based distinctiveness estimation, outperforms all relevant baselines in effectiveness, efficiency, and scalability. A case study on two downstream ML tasks also highlights its potential to find datasets with more useful tuples to enhance the performance of ML tasks.

Abstract Table integration aims to create a comprehensive table by consolidating tuples containing relevant information. In this work, we investigate the challenge of integrating multiple tables from a data lake, focusing on three core tasks: (1) pairwise integrability judgment , which determines whether a tuple pair is integrable, accounting for any occurrences of semantic equivalence or typographical errors; (2) integrable set discovery , which identifies all integrable sets in a table based on pairwise integrability judgments established in the first task; (3) multi-tuple conflict resolution , which resolves conflicts between multiple tuples during integration. To this end, we train a binary classifier to address the task of pairwise integrability judgment. Given the scarcity of labeled data in data lakes, we propose a self-supervised adversarial contrastive learning algorithm to perform classification, which incorporates data augmentation methods and adversarial examples to autonomously generate new training data. Upon the output of pairwise integrability judgment, each integrable set can be considered as a community—a densely connected sub-graph where nodes and edges correspond to tuples in the table and their pairwise integrability, respectively—we proceed to investigate various community detection algorithms to address the integrable set discovery objective. Moving forward to tackle multi-tuple conflict resolution , we introduce an innovative in-context learning methodology. This approach capitalizes on the knowledge embedded within large language models to effectively resolve conflicts that arise when integrating multiple tuples. Notably, our method minimizes the need for annotated data, making it particularly suited for scenarios where labeled datasets are scarce. Since no suitable test collections are available for our tasks, we develop our own benchmarks using two real-world dataset repositories: Real and Join . We conduct extensive experiments on these benchmarks to validate the robustness and applicability of our methodologies in the context of integrating tables within data lakes.

Large language models (LLMs) are capable of assessing document and query characteristics, including relevance, and are now being used for a variety of different classification labeling tasks as well. This study explores how to use LLMs to classify an information need, often represented as a user query. In particular, our goal is to classify the cognitive complexity of the search task for a given “backstory”. Using 180 TREC topics and backstories, we show that GPT-based LLMs agree with human experts as much as other human experts. We also show that batching and ordering can significantly impact the accuracy of GPT-3.5, but rarely alter the quality of GPT-4 predictions. This study provides insights into the efficacy of large language models for annotation tasks normally completed by humans, and offers recommendations for other similar applications.

While in-memory learned indexes have shown promising performance as compared to B+-tree, most widely used databases in real applications still rely on disk-based operations. From our experiments, we observe that directly applying the ex-isting in-memory learned indexes into on-disk setting suffers from several drawbacks and cannot outperform a standard B+-tree in most cases. Therefore, we make the first attempt to show how the idea of learned index can benefit the on-disk index by proposing AULID, a fully on-disk updatable learned index that can achieve state-of-the-art performance across multiple workload types. The AULID approach combines the benefits from both traditional indexing techniques and the learned indexes to reduce the I/O cost - the main overhead under disk setting. Specifically, three aspects are taken into consideration in reducing I/O costs: (1) reduce the overhead in updating the index structure; (2) induce shorter paths from root to leaf node; (3) achieve better locality to minimize the number of block reads required to complete a scan. Five principles are proposed to guide the design of AULID which shows remarkable performance gains and meanwhile is easy to implement. Our evaluation shows that AULI D has comparable storage costs to a B+-tree and is much smaller than other learned indexes, and AULID is up to 2.11x, 8.63x, 1.72x, 5.51x, and 8.02x more efficient than FITing-tree, PGM, B+-tree, ALEX, and LIPP.

Line charts are fundamental to data analysis and exploration, offering concise visual representations of trends. However, gaining access to the underlying data used to construct these charts is often challenging. In this paper, we describe DDLC (short for Dataset discovery via line charts), an automatic dataset discovery tool that is able to not only identify datasets (from a dataset repository) that are "relevant" to the information depicted from a line chart provided by the users, but also empower users to refine search results based on specific visual elements extracted from the line chart. Moreover, DDLC offers multiple avenues for users to validate search outcomes: 1) Providing explanations on how a similar line chart could be generated from the identified dataset; 2) enabling comparison of line charts generated from different datasets via different ways (e.g., the aggregation vs. non-aggregation operator); 3) facilitating fine-grained examination of the correspondence between the line chart and the identified dataset. By seamlessly combining dataset retrieval with visual refinement and validation mechanisms, DDLC offers a comprehensive solution for the data-driven exploration and analysis.

In this paper, we study how to acquire labeled data points from a large data pool to enrich a training set for enhancing supervised machine learning (ML) performance. The state-of-the-art solution is the clustering-based training set selection (CTS) algorithm, which initially clusters the data points in a data pool and subsequently selects new data points from clusters. The efficiency of CTS is constrained by its frequent retraining of the target ML model, and the effectiveness is limited by the selection criteria, which represent the state of data points within each cluster and impose a restriction of selecting only one cluster in each iteration. To overcome these limitations, we propose a new algorithm, called CTS with incremental estimation of adaptive score (IAS). IAS employs online learning, enabling incremental model updates by using new data, and eliminating the need to fully retrain the target model, and hence improves the efficiency. To enhance the effectiveness of IAS, we introduce adaptive score estimation, which serves as novel selection criteria to identify clusters and select new data points by balancing trade-offs between exploitation and exploration during data acquisition. To further enhance the effectiveness of IAS, we introduce a new adaptive mini-batch selection method that, in each iteration, selects data points from multiple clusters rather than a single cluster, hence eliminating the potential bias due to using only one cluster. By integrating this method into the IAS algorithm, we propose a novel algorithm termed IAS with adaptive mini-batch selection (IAS-AMS). Experimental results highlight the superior effectiveness of IAS-AMS, with IAS also outperforming other competing algorithms. In terms of efficiency, IAS takes the lead, while the efficiency of IAS-AMS is on par with that of the existing CTS algorithm.

In this paper, we propose a novel facility relocation problem where facilities (and their services) are portable, which is a combinatorial search problem with many practical applications. Given a set of users, a set of existing facilities, and a set of potential sites, we decide which of the existing facilities to relocate to potential sites, such that two factors are satisfied: (1) facility exposure: facilities after relocation have balanced exposure, namely serving equivalent numbers of users; (2) user convenience: it is convenient for users to access the nearest facility, which provides services with shorter travel distance. This problem is motivated by applications such as dynamically redistributing vaccine resources to align supply with demand for different vaccination centers, and relocating the bike sharing sites daily to improve the transportation efficiency. We first prove that this problem is NP-hard, and then we propose two algorithms: a non-learning best response algorithm () and a reinforcement learning algorithm (). In particular, the best response algorithm finds a Nash equilibrium to balance the facility-related and the user-related goals. To avoid being confined to only one Nash equilibrium, as found in the method, we also propose the reinforcement learning algorithm for long-term benefits, where each facility is an agent and we determine whether a facility needs to be relocated or not. To verify the effectiveness of our methods, we adopt multiple metrics to evaluate not only our objective, but also several other facility exposure equity and user convenience metrics to understand the benefits after facility relocation. Finally, comprehensive experiments using real-world datasets provide insights into the effectiveness of the two algorithms in practice.

Although many updatable learned indexes have been proposed in recent years, whether they can outperform traditional approaches on disk remains unknown. In this study, we revisit and implement four state-of-the-art updatable learned indexes on disk, and compare them against the B+-tree under a wide range of settings. Through our evaluation, we make some key observations: 1) Overall, the B+-tree performs well across a range of workload types and datasets. 2) A learned index could outperform B+-tree or other learned indexes on disk for a specific workload. For example, PGM achieves the best performance in write-only workloads while LIPP significantly outperforms others in lookup-only workloads. We further conduct a detailed performance analysis to reveal the strengths and weaknesses of these learned indexes on disk. Moreover, we summarize the observed common shortcomings in five categories and propose four design principles to guide future design of on-disk, updatable learned indexes: (1) reducing the index's tree height, (2) better data structures to lower operation overheads, (3) improving the efficiency of scan operations, and (4) more efficient storage layout.

In this work, we study how to find the k most representative routes over large scale trajectory data, which is a fundamental operation that benefits various real-world applications, such as traffic monitoring and public transportation planning. The operator is time-sensitive as it must be able to adapt the results as traffic conditions change. We first prove the NP-hardness of the problem, and then propose a range of effective approximate solutions that have rapid response times. Specifically, we first build a lookup table that stores the trajectories covered by each edge in a given road network. Rather than performing a depth-first search for all possible routes, we find a 1/η approximate solution by developing a maximum-weight algorithm. Since each edge in a route may be close to several trajectories, we further propose a coverage-first algorithm to locate the edges with the greatest coverage gain in the solution route set. By observing that in the real world each edge is connected to only a few other edges in a road network, we have developed a connect-first algorithm that finds consecutive edges for k representative routes by greedily selecting edges with the maximum marginal gain for each route. Finally, comprehensive experiments over two real-world datasets are conducted to verify the effectiveness and efficiency of our proposed algorithms, and provide evidence of the usefulness of our solution and rapid response times in traffic monitoring tasks.

Any given information need can be expressed via a wide range of possible queries. Recent work with such query variations has demonstrated that different queries can fetch notably divergent sets of documents, even when the queries have identical intents and superficial similarity. That is, different users might receive SERPs of quite different effectiveness for the same information need. That observation then raises an interesting question: do users have a sense of how useful any given query will be? Can they anticipate the effectiveness of alternative queries for the same retrieval need? To explore that question we designed and carried out a crowd-sourced user study in which we asked subjects to consider an information need statement expressed as a backstory, and then provide their opinions as to the relative usefulness of a set of queries ostensibly addressing that objective. We solicited opinions using two different interfaces: one that collected absolute ratings of queries, and one that required that the subjects place a set of queries into "order". We found that crowd workers are reasonably consistent in their estimates of how effective queries are likely to be, and also that their estimates correlate positively with actual system performance.

Abstract Query performance prediction (QPP) has been studied extensively in the IR community over the last two decades. A by-product of this research is a methodology to evaluate the effectiveness of QPP techniques. In this paper, we re-examine the existing evaluation methodology commonly used for QPP, and propose a new approach. Our key idea is to model QPP performance as a distribution instead of relying on point estimates. To obtain such distribution, we exploit the scaled Absolute Ranking Error (sARE) measure, and its mean the scaled Mean Absolute Ranking Error (sMARE). Our work demonstrates important statistical implications, and overcomes key limitations imposed by the currently used correlation-based point-estimate evaluation approaches. We also explore the potential benefits of using multiple query formulations and ANalysis Of VAriance (ANOVA) modeling in order to measure interactions between multiple factors. The resulting statistical analysis combined with a novel evaluation framework demonstrates the merits of modeling QPP performance as distributions, and enables detailed statistical ANOVA models for comparative analyses to be created.

Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures realtime traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as trajectory-driven traffic bottleneck identification : Given a road network R , a trajectory database T , find a representative set of seed edges of size K of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic diffusion probability between two connected segments and the residual ratio of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm BF , with an approximation ratio of 1-1/ e . To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm SG . Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods.

Several recent studies have explored the interaction effects between topics, systems, corpora, and components when measuring retrieval effectiveness. However, all of these previous studies assume that a topic or information need is represented by a single query. In reality, users routinely reformulate queries to satisfy an information need. In recent years, there has been renewed interest in the notion of “query variations” which are essentially multiple user formulations for an information need. Like many retrieval models, some queries are highly effective while others are not. This is often an artifact of the collection being searched which might be more or less sensitive to word choice. Users rarely have perfect knowledge about the underlying collection, and so finding queries that work is often a trial-and-error process. In this work, we explore the fundamental problem of system interaction effects between collections, ranking models, and queries. To answer this important question, we formalize the analysis using ANalysis Of VAriance (ANOVA) models to measure multiple components effects across collections and topics by nesting multiple query variations within each topic. Our findings show that query formulations have a comparable effect size of the topic factor itself, which is known to be the factor with the greatest effect size in prior ANOVA studies. Both topic and formulation have a substantially larger effect size than any other factor, including the ranking algorithms and, surprisingly, even query expansion. This finding reinforces the importance of further research in understanding the role of query rewriting in IR related tasks.

Search engine users retrieve relevant information for an information need using keyword queries. Different users may have similar information needs, but use different query terms. The resulting user query variations can provide a wealth of useful information to IR researchers. Most recently, the keystroke-level telemetry data gathered as part of the CC-News-En collection provides important insights into how users create queries for a search task, at a level of detail not possible using a normal query log. In this demo, we present an interactive tool that enables practitioners to visualize users formulating queries. Our new tool is a temporal simulation of the typing behavior of crowdworkers, grouped by information need. It provides the ability to directly compare the cognitive behavior of multiple users simultaneously, and observe how query keyword selection and ordering happens before a final query is submitted to a search engine. To demonstrate the benefit of our tool, we include a qualitative study of four different user behavior patterns which were observed in the CC-News-En collection.

Accurately estimating the retrieval effectiveness of different queries representing distinct information needs is a problem in Information Retrieval (IR) that has been studied for over 20 years. Recent work showed that the problem can be significantly harder when multiple queries representing the same information need are used in prediction. By generalizing the existing evaluation framework of Query Performance Prediction (QPP) we explore the causes of these differences in prediction quality in the two scenarios. Our empirical analysis demonstrates that for most predictors, this difference is solely an artifact of the underlying differences in the query effectiveness distributions. Our detailed analysis also demonstrates key performance distribution properties under which (QPP) is most and least reliable.

Information Retrieval has a long history of applying either discriminative or generative modeling to retrieval and ranking tasks. Recent developments in transformer architectures and multi-task learning techniques have dramatically improved our ability to train effective neural models capable of resolving a wide variety of tasks using either of these paradigms. In this paper, we propose a novel multi-task learning approach which can be used to produce more effective neural ranking models. The key idea is to improve the quality of the underlying transformer model by cross-training a retrieval task and one or more complementary language generation tasks. By targeting the training on the encoding layer in the transformer architecture, our experimental results show that the proposed multi-task learning approach consistently improves retrieval effectiveness on the targeted collection and can easily be re-targeted to new ranking tasks. We provide an in-depth analysis showing how multi-task learning modifies model behaviors, resulting in more general models.

In this paper, we propose and study a variant of the dynamic ridesharing problem with a specific focus on peak hours: Given a set of drivers and a set of rider requests, we aim to match drivers to each rider request by achieving two objectives: maximizing the served rate and minimizing the total additional distance, subject to a series of spatio-temporal constraints. Our problem can be distinguished from existing ridesharing solutions in three aspects: (1) Previous work did not fully explore the impact of peak travel periods where the number of rider requests is much greater than the number of available drivers. (2) Existing ridesharing solutions usually rely on single objective optimization techniques, such as minimizing the total travel cost (either distance or time). (3) When evaluating the overall system performance, the runtime spent on updating drivers' trip schedules as per newly coming rider requests should be incorporated, while it is unfortunately excluded by most existing solutions. In order to achieve our goal, we propose an underlying index structure on top of a partitioned road network, and compute the lower bounds of the shortest path distance between any two vertices. Using the proposed index together with a set of new pruning rules, we develop an efficient algorithm to dynamically include new riders directly into an existing trip schedule of a driver. In order to respond to new rider requests more effectively, we propose two algorithms that bilaterally match drivers with rider requests. Finally, we perform extensive experiments on a large-scale test collection to validate the effectiveness and efficiency of the proposed methods.

Feature engineering is a fundamental but poorly documented component in Learning-to-Rank (LTR) search engines. Such features are commonly used to construct learning models for web and product search engines, recommender systems, and question-answering tasks. In each of these domains, there is a growing interest in the creation of open-access test collections that promote reproducible research. However, there are still few open-source software packages capable of extracting high-quality machine learning features from large text collections. Instead, most feature-based LTR research relies on "canned" test collections, which often do not expose critical details about the underlying collection or implementation details of the extracted features. Both of these are crucial to collection creation and deployment of a search engine into production. So in this regard, the experiments are rarely reproducible with new features or collections, or helpful for companies wishing to deploy LTR systems.

We describe a static, open-access news corpus using data from the Common Crawl Foundation, who provide free, publicly available web archives, including a continuous crawl of international news articles published in multiple languages. Our derived corpus, CC-News-En, contains 44 million English documents collected between September 2016 and March 2018. The collection is comparable in size with the number of documents typically found in a single shard of a large-scale, distributed search engine, and is four times larger than the news collections previously used in offline information retrieval experiments. To complement the corpus, 173 topics were curated using titles from Reddit threads, forming a temporally representative sampling of relevant news topics over the 583 day collection window. Information needs were then generated using automatic summarization tools to produce textual and audio representations, and used to elicit query variations from crowdworkers, with a total of 10,437 queries collected against the 173 topics. Of these, 10,089 include key-stroke level instrumentation that captures the timings of character insertions and deletions made by the workers while typing their queries. These new resources support a wide variety of experiments, including large-scale efficiency exercises and query auto-completion synthesis, with scope for future addition of relevance judgments to support offline effectiveness experiments and hence batch evaluation campaigns.

Network embedding is an effective method to learn low-dimensional representations of nodes, which can be applied to various real-life applications such as visualization, node classification, and link prediction. Although significant progress has been made on this problem in recent years, several important challenges remain, such as how to properly capture temporal information in evolving networks. In practice, most networks are continually evolving. Some networks only add new edges or nodes such as authorship networks, while others support removal of nodes or edges such as internet data routing. If patterns exist in the changes of the network structure, we can better understand the relationships between nodes and the evolution of the network, which can be further leveraged to learn node representations with more meaningful information. In this paper, we propose the Embedding via Historical Neighborhoods Aggregation (EHNA) algorithm. More specifically, we first propose a temporal random walk that can identify relevant nodes in historical neighborhoods which have impact on edge formations. Then we apply a deep learning model which uses a custom attention mechanism to induce node embeddings that directly capture temporal information in the underlying feature representation. We perform extensive experiments on a range of real-world datasets, and the results demonstrate the effectiveness of our new approach in the network reconstruction task and the link prediction task.

Information retrieval (IR) ranking models in production systems continually evolve in response to user feedback, insights from research, and new developments. Rather than investing all engineering resources to produce a single challenger to the existing system, a commercial provider might choose to explore multiple new ranking models simultaneously. However, even small changes to a complex model can have unintended consequences. In particular, the per-topic effectiveness profile is likely to change, and even when an overall improvement is achieved, gains are rarely observed for every query, introducing the risk that some users or queries may be negatively impacted by the new model if deployed into production.

Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularity and thereby are heterogeneous. For example, one user may prefer to be recommended a region (say Manhattan), while another user might prefer a venue (say a restaurant). Even for the same user, preferences can change at different stages of data exploration. In this paper, we study how to support top-k spatial object recommendations at varying levels of spatial granularity, enabling spatial objects at varying granularity, such as a city, suburb, or building, as a Point of Interest (POI). To solve this problem, we propose the use of a POI tree, which captures spatial containment relationships between POIs. We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level. Each task consists of two subtasks: (i) attribute-based representation learning; (ii) interaction-based representation learning. The first subtask learns the feature representations for both users and POIs, capturing attributes directly from their profiles. The second subtask incorporates user-POI interactions into the model. Additionally, MPR can provide insights into why certain recommendations are being made to a user based on three types of hints: user-aspect, POI-aspect, and interaction-aspect. We empirically validate our approach using two real-life datasets, and show promising performance improvements over several state-of-the-art methods.

Influence maximization (IM) continues to be a key research problem in social networks. The goal is to find a small seed set of target users that have the greatest influence in the network under various stochastic diffusion models. While significant progress has been made on the IM problem in recent years, several interesting challenges remain. For example, social networks in reality are constantly evolving, and "important" users with the most influence also change over time. As a result, several recent studies have proposed approaches to update the seed set as the social networks evolve. However, this seed set is not guaranteed to be the best seed set over a period of time. In this paper we study the problem of Distinct Influence Maximization (DIM) where the goal is to identify a seed set of influencers who maximize the number of distinct users influenced over a predefined window of time. Our new approach allows social network providers to make fewer incremental changes to targeted advertising while still maximizing the coverage of the advertisements. It also provides finer grained control over service level agreements where a certain number of impressions for an advertisement must be displayed in a specific time period. We propose two different strategies HCS and VCS with novel graph compression techniques to solve this problem. Additionally, VCS can also be applied directly to the traditional IM problem. Extensive experiments on real-world datasets verify the efficiency, accuracy and scalability of our solutions on both the DIM and IM problems.

Processing top-k bag-of-words queries is critical to many information retrieval applications, including web-scale search. In this work, we consider algorithmic properties associated with dynamic pruning mechanisms. Such algorithms maintain a score threshold (the k th highest similarity score identified so far) so that low-scoring documents can be bypassed, allowing fast top-k retrieval with no loss in effectiveness. In standard pruning algorithms the score threshold is initialized to the lowest possible value. To accelerate processing, we make use of term- and query-dependent features to predict the final value of that threshold, and then employ the predicted value right from the commencement of processing. Because of the asymmetry associated with prediction errors (if the estimated threshold is too high the query will need to be re-executed in order to assure the correct answer), the prediction process must be risk-sensitive. We explore techniques for balancing those factors, and provide detailed experimental results that show the practical usefulness of the new approach.

In a test collection setting, topic difficulty can be defined as the average effectiveness of a set of systems for a topic. In this paper we study the effects on the topic difficulty of: (i) the set of retrieval systems; (ii) the underlying document corpus; and (iii) the system components. By generalizing methods recently proposed to study system component factor analysis, we perform a comprehensive analysis on topic difficulty and the relative effects of systems, corpora, and component interactions. Our findings show that corpora have the most significant effect on topic difficulty.

The query performance prediction (QPP) task is to estimate the effectiveness of a search performed in response to a query with no relevance judgments. Existing QPP methods do not account for the effectiveness of a query in representing the underlying information need. We demonstrate the far-reaching implications of this reality using standard TREC-based evaluation of QPP methods: their relative prediction quality patterns vary with respect to the effectiveness of queries used to represent the information needs. Motivated by our findings, we revise the basic probabilistic formulation of the QPP task by accounting for the information need and its connection to the query. We further explore this connection by proposing a novel QPP approach that utilizes information about a set of queries representing the same information need. Predictors instantiated from our approach using a wide variety of existing QPP methods post prediction quality that substantially transcends that of applying these methods, as is standard, using a single query representing the information need. Additional in-depth empirical analysis of different aspects of our approach further attests to the crucial role of query effectiveness in QPP.

Reducing excessive costs in feature acquisition and model evaluation has been a long-standing challenge in learning-to-rank systems. A cascaded ranking architecture turns ranking into a pipeline of multiple stages, and has been shown to be a powerful approach to balancing efficiency and effectiveness trade-offs in large-scale search systems. However, learning a cascade model is often complex, and usually performed stagewise independently across the entire ranking pipeline. In this work we show that learning a cascade ranking model in this manner is often suboptimal in terms of both effectiveness and efficiency. We present a new general framework for learning an end-to-end cascade of rankers using backpropagation. We show that stagewise objectives can be chained together and optimized jointly to achieve significantly better trade-offs globally. This novel approach is generalizable to not only differentiable models but also state-of-the-art tree-based algorithms such as LambdaMART and cost-efficient gradient boosted trees, and it opens up new opportunities for exploring additional efficiency-effectiveness trade-offs in large-scale search systems.

In this paper, we study the problem of large-scale trajectory data clustering,k-paths, which aims to efficiently identifyk"representative" paths in a road network. Unlike traditional clustering approaches that require multiple data-dependent hyperparameters,k-paths can be used for visual exploration in applications such as traffic monitoring, public transit planning, and site selection. By combining map matching with an efficient intermediate representation of trajectories and a noveledge-based distance(EBD) measure, we present a scalable clustering method to solvek-paths. Experiments verify that we can cluster millions of taxi trajectories in less than one minute, achieving improvements of up to two orders of magnitude over state-of-the-art solutions that solve similar trajectory clustering problems.

Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set. Query variations covering the same information need represent one way in which different sources of information might arise. However, when implemented in the obvious manner, fusion over query variations is not cost-effective, at odds with the usual web-search requirement for strict per-query efficiency guarantees. In this work, we propose a novel solution to query fusion by splitting the computation into two parts: one phase that is carried out offline, to generate pre-computed centroid answers for queries addressing broadly similar information needs, and then a second online phase that uses the corresponding topic centroid to compute a result page for each query. To achieve this, we make use of score-based fusion algorithms whose costs can be amortized via the pre-processing step and that can then be efficiently combined during subsequent per-query re-ranking operations. Experimental results using the ClueWeb12B collection and the UQV100 query variations demonstrate that centroid-based approaches allow improved retrieval effectiveness at little or no loss in query throughput or latency and within reasonable pre-processing requirements. We additionally show that queries that do not match any of the pre-computed clusters can be accurately identified and efficiently processed in our proposed ranking pipeline.

Consistency of relevance judgments is a vital issue for the construction of test collections in information retrieval. As human relevance assessments are costly, and large collections can contain many documents of varying relevance, collecting reliable judgments is a critical component to building reusable test collections. We explore the impact of document presentation order on human relevance assessments. Our primary goal is to determine if assessor disagreement can be minimized through the order in which documents are presented to assessors. To achieve this goal, we compare two commonly used presentation orderings with a new ordering designed to aid assessors to more easily discriminate between relevant and non-relevant documents. By carefully controlling the presentation ordering, assessors can more quickly converge on a consistent notion of relevance during the assessment exercise, leading to higher overall judging agreement. In addition, important interactions between presentation ordering and topic difficulty on assessor agreement are highlighted. Our findings suggest that document presentation order does indeed have a substantial impact on assessor agreement , and that our new ordering is more robust than previous approaches across a variety of different topic types.

We study a new kind of query - a Reverse k Nearest Neighbor Search over Trajectories (RkNNT), which can be used for route planning and capacity estimation in the transportation field. Given a set of existing routes DR, a set of passenger transitions DT, and a query route Q, an RkNNT query returns all transitions that take Q as one of its k nearest travel routes. We develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data is available for answering an RkNNT query using a filter-refine processing framework. Further, an application of using RkNNT to plan the optimal route in bus networks, namely MaxRkNNT, is proposed and studied. Experiments on real datasets demonstrate the efficiency and scalability of our approaches. In the future, the RkNNT can be extended to applied to the traffic prediction.

The problem of influence maximization has attracted a lot of attention as it provides a way to improve marketing, branding, and product adoption. However, existing studies rarely consider the physical locations of the social users, although location is an important factor in targeted marketing. In this paper, we investigate the problem of influence spanning maximization in location-aware social networks. Our target is to identify the maximum spanning geographical regions in a query region, which is very different from the existing methods that focus on the quantity of the activated users in the query region. Since the problem is NP-hard, we develop one greedy algorithm with a 1-1/e approximation ratio and further improve its efficiency by developing an upper bound based approach. Then, we propose the OIR index by combining ordered influential node lists and an R*-tree and design the index based solution. The efficiency and effectiveness of our proposed solutions and index have been verified using three real datasets.

This paper presents a new trajectory search engine called Torch for querying road network trajectory data. Torch is able to efficiently process two types of typical queries (similarity search and Boolean search), and support a wide variety of trajectory similarity functions. Additionally, we propose a new similarity function LORS in Torch to measure the similarity in a more effective and efficient manner. Indexing and search in Torch works as follows. First, each raw vehicle trajectory is transformed to a set of road segments (edges) and a set of crossings (vertices) on the road network. Then a lightweight edge and vertex index called LEVI is built. Given a query, a filtering framework over LEVI is used to dynamically prune the trajectory search space based on the similarity measure imposed. Finally, the result set (ranked or Boolean) is returned. Extensive experiments on real trajectory datasets verify the effectiveness and efficiency of Torch.

Fusion is an important and central concept in Information Retrieval. The goal of fusion methods is to merge different sources of information so as to address a retrieval task. For example, in the adhoc retrieval setting, fusion methods have been applied to merge multiple document lists retrieved for a query. The lists could be retrieved using different query representations, document representations, ranking functions and corpora. The goal of this half day, intermediate-level, tutorial is to provide a methodological view of the theoretical foundations of fusion approaches, the numerous fusion methods that have been devised and a variety of applications for which fusion techniques have been applied.

Selective search architectures use resource selection algorithms such as Rank-S or Taily to rank index shards and determine how many to search for a given query. Most prior research evaluated solutions by their ability to improve efficiency without significantly reducing early-precision metrics such as [email protected] and [email protected] This paper recasts selective search as an early stage of a multi-stage retrieval architecture, which makes recall-oriented metrics more appropriate. A new algorithm is presented that predicts the number of shards that must be searched for a given query in order to meet recall-oriented goals. Decoupling shard ranking from deciding how many shards to search clarifies efficiency vs. effectiveness trade-offs, and enables them to be optimized independently. Experiments on two corpora demonstrate the value of this approach.

Predicting the performance of a search engine for a given query is a fundamental and challenging task in information retrieval. Accurate performance predictors can be used in various ways, such as triggering an action, choosing the most effective ranking function per query, or selecting the best variant from multiple query formulations. In this paper, we propose a general end-to-end query performance prediction framework based on neural networks, called NeuralQPP. Our framework consists of multiple components, each learning a representation suitable for performance prediction. These representations are then aggregated and fed into a prediction sub-network. We train our models with multiple weak supervision signals, which is an unsupervised learning approach that uses the existing unsupervised performance predictors using weak labels. We also propose a simple yet effective component dropout technique to regularize our model. Our experiments on four newswire and web collections demonstrate that NeuralQPP significantly outperforms state-of-the-art baselines, in nearly every case. Furthermore, we thoroughly analyze the effectiveness of each component, each weak supervision signal, and all resulting combinations in our experiments.

Large scale retrieval systems often employ cascaded ranking architectures, in which an initial set of candidate documents are iteratively refined and re-ranked by increasingly sophisticated and expensive ranking models. In this paper, we propose a unified framework for predicting a range of performance-sensitive parameters based on minimizing end-to-end effectiveness loss. The framework does not require relevance judgments for training, is amenable to predicting a wide range of parameters, allows for fine tuned efficiency-effectiveness trade-offs, and can be easily deployed in large scale search systems with minimal overhead. As a proof of concept, we show that the framework can accurately predict a number of performance parameters on a query-by-query basis, allowing efficient and effective retrieval, while simultaneously minimizing the tail latency of an early-stage candidate generation system. On the 50 million document ClueWeb09B collection, and across 25,000 queries, our hybrid system can achieve superior early-stage efficiency to fixed parameter systems without loss of effectiveness, and allows more finely-grained efficiency-effectiveness trade-offs across the multiple stages of the retrieval system.

With the widespread use of GPS-enabled mobile devices, an unprecedented amount of trajectory data has become available from various sources such as Bikely, GPS-wayPoints, and Uber. The rise of smart transportation services and recent break-throughs in autonomous vehicles increase our reliance on trajectory data in a wide variety of applications. Supporting these services in emerging platforms requires more efficient query processing in trajectory databases. In this paper, we propose two new coverage queries for trajectory databases: (i) k Best Facility Trajectory Search ( k BFT); and (ii) k Best Coverage Facility Trajectory Search ( k BCovFT). We propose a novel index structure, the Trajectory Quadtree (TQ-tree) that utilizes a quadtree to hierarchically organize trajectories into different nodes, and then applies a z-ordering to further organize the trajectories by spatial locality inside each node. This structure is highly effective in pruning the trajectory search space, which is of independent interest. By exploiting the TQ-tree, we develop a divide-and-conquer approach to efficiently process a k BFT query. To solve the k BCovFT, which is a non-submodular NP-hard problem, we propose a greedy approximation. We evaluate our algorithms through an extensive experimental study on several real datasets, and demonstrate that our algorithms outperform baselines by two to three orders of magnitude.

GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. In this paper, we study a new kind of query-Reverse k Nearest Neighbor Search over Trajectories (RkNNT), which can be used for route planning and capacity estimation. Given a set of existing routes DR, a set of passenger transitions DT, and a query route Q, an RkNNT query returns all transitions that take Q as one of its k nearest travel routes. To solve the problem, we first develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data are available for answering an RkNNT query. Then we introduce a filter refinement framework for processing RkNNT queries using the proposed indexes. Next, we show how to use RkNNT to solve the optimal route planning problem MaxRkNNT (MinRkNNT), which is to search for the optimal route from a start location to an end location that could attract the maximum (or minimum) number of passengers based on a predefined travel distance threshold. Experiments on real datasets demonstrate the efficiency and scalability of our approaches. To the best of our knowledge, this is the first work to study the RkNNT problem for route planning.

In this paper, we propose and study the problem of top-m rank aggregation of spatial objects in streaming queries, where, given a set of objects O, a stream of spatial queries (kNN or range), the goal is to report the m objects with the highest aggregate rank. The rank of an object with respect to an individual query is computed based on its distance from the query location, and the aggregate rank is computed from all of the individual rank orderings. In order to solve this problem, we show how to upper and lower bound the rank of an object for any unseen query. Then we propose an approximation solution to continuously monitor the top-m objects efficiently, for which we design an Inverted Rank File (IRF) index to guarantee the error bound of the solution. In particular, we propose the notion of safe ranking to determine whether the current result is still valid or not when new queries arrive, and propose the notion of validation objects to limit the number of objects to update in the top-m results. We also propose an exact solution for applications where an approximate solution is not sufficient. Last, we conduct extensive experiments to verify the efficiency and effectiveness of our solutions. This is a fundamental problem that draws inspiration from three different domains: rank aggregation, continuous queries and spatial databases, and the solution can be used to monitor the importance / popularity of spatial objects, which in turn can provide new analytical tools for spatial data.

Social networks have become a vital mechanism to disseminate information to friends and colleagues. But the dynamic nature of information and user connectivity within these networks raised many new and challenging research problems. One of them is the query-related topic search in social networks. In this work, we investigate the important problem of the personalized influential topic search. There are two challenging questions that need to be answered: how to extract the social summarization of the social network so as to measure the topics' influence at the similar granularity scale? and how to apply the social summarization to the problem of personalized influential topic search. Based on the evaluation using real-world datasets, our proposed algorithms are proved to efficient and effective.

We study a new type of spatial-textual trajectory search: the Exemplar Trajectory Query (ETQ), which specifies one or more places to visit, and descriptions of activities at each place. Our goal is to efficiently find the top-k trajectories by computing spatial and textual similarity at each point. The computational cost for pointwise matching is significantly higher than previous approaches. Therefore, we introduce an incremental pruning baseline and explore how to adaptively tune our approach, introducing a gap-based optimization and a novel twolevel threshold algorithm to improve efficiency. Our proposed methods support order-sensitive ETQ with a minor extension. Experiments on two datasets verify the efficiency and scalability of our proposed solution.

Increasing test collection sizes and limited judgment budgets create measurement challenges for IR batch evaluations, challenges that are greater when using deep effectiveness metrics than when using shallow metrics, because of the increased likelihood that unjudged documents will be encountered. Here we study the problem of metric score adjustment, with the goal of accurately estimating system performance when using deep metrics and limited judgment sets, assuming that dynamic score adjustment is required per topic due to the variability in the number of relevant documents. We seek to induce system orderings that are as close as is possible to the orderings that would arise if full judgments were available. Starting with depth-based pooling, and no prior knowledge of sampling probabilities, the first phase of our two-stage process computes a background gain for each document based on rank-level statistics. The second stage then accounts for the distributional variance of relevant documents. We also exploit the frequency statistics of pooled relevant documents in order to determine a threshold for dynamically determining the set of topics to be adjusted. Taken together, our results show that: (i) better score estimates can be achieved when compared to previous work; (ii) by setting a global threshold, we are able to adapt our methods to different collections; and (iii) the proposed estimation methods reliably approximate the system orderings achieved when many more relevance judgments are available. We also consider pools generated by a two-strata sampling approach.

Complex machine learning models are now an integral part of modern, large-scale retrieval systems. However, collection size growth continues to outpace advances in efficiency improvements in the learning models which achieve the highest effectiveness. In this paper, we re-examine the importance of tightly integrating feature costs into multi-stage learning-to-rank (LTR) IR systems. We present a novel approach to optimizing cascaded ranking models which can directly leverage a variety of different state-of-the-art LTR rankers such as LambdaMART and Gradient Boosted Decision Trees. Using our cascade model, we conclusively show that feature costs and the number of documents being re-ranked in each stage of the cascade can be balanced to maximize both efficiency and effectiveness. Finally, we also demonstrate that our cascade model can easily be deployed on commonly used collections to achieve state-of-the-art effectiveness results while only using a subset of the features required by the full model.

In recent years, gathering relevance judgments through non-topic originators has become an increasingly important problem in Information Retrieval. Relevance judgments can be used to measure the effectiveness of a system, and are often needed to build supervised learning models in learning-to-rank retrieval systems. The two most popular approaches to gathering bronze level judgments - where the judge is not the originator of the information need for which relevance is being assessed, and is not a topic expert - is through a controlled user study, or through crowdsourcing. However, judging comes at a cost (in time, and usually money) and the quality of the judgments can vary widely. In this work, we directly compare the reliability of judgments using three different types of bronze assessor groups. Our first group is a controlled Lab group; the second and third are two different crowdsourcing groups, CF-Document where assessors were free to judge any number of documents for a topic, and CF-Topic where judges were required to judge all of the documents from a single topic, in a manner similar to the Lab group. Our study shows that Lab assessors exhibit a higher level of agreement with a set of ground truth judgments than CF-Topic and CF-Document assessors. Inter-rater agreement rates show analogous trends. These finding suggests that in the absence of ground truth data, agreement between assessors can be used to reliably gauge the quality of relevance judgments gathered from secondary assessors, and that controlled user studies are more likely to produce reliable judgments despite being more costly.

We present an empirical comparison between document-at-a-time (DaaT) and score-at-a-time (SaaT) document ranking strategies within a common framework. Although both strategies have been extensively explored, the literature lacks a fair, direct comparison: such a study has been difficult due to vastly different query evaluation mechanics and index organizations. Our work controls for score quantization, document processing, compression, implementation language, implementation effort, and a number of details, arriving at an empirical evaluation that fairly characterizes the performance of three specific techniques: WAND (DaaT), BMW (DaaT), and JASS (SaaT). Experiments reveal a number of interesting findings. The performance gap between WAND and BMW is not as clear as the literature suggests, and both methods are susceptible to tail queries that may take orders of magnitude longer than the median query to execute. Surprisingly, approximate query evaluation in WAND and BMW does not significantly reduce the risk of these tail queries. Overall, JASS is slightly slower than either WAND or BMW, but exhibits much lower variance in query latencies and is much less susceptible to tail query effects. Furthermore, JASS query latency is not particularly sensitive to the retrieval depth, making it an appealing solution for performance-sensitive applications where bounds on query latencies are desirable.

Influence maximization is a recent but well-studied problem which helps identify a small set of users that are most likely to “influence” the maximum number of users in a social network. The problem has attracted a lot of attention as it provides a way to improve marketing, branding, and product adoption. However, existing studies rarely consider the physical locations of the users, but location is an important factor in targeted marketing. In this paper, we propose and investigate the problem of influence maximization in location-aware social networks, or, more generally,Geo-social Influence Spanning Maximization. Given a query$q$composed of a region$R$, a regional acceptance rate$\rho$, and an integer$k$as a seed selection budget, our aim is to find the maximum geographic spanning regions (MGSR). We refer to this as the MGSR problem. Our approach differs from previous work as we focus more on identifying the maximum spanning geographical regions within a region$R$, rather than just the number of activated users in the given network like the traditional influence maximization problem[14]. Our research approach can be effectively used for online marketing campaigns that depend on the physical location of social users. To address the MGSR problem, we first prove NP-Hardness. Next, we present a greedy algorithm with a$1-1/e$approximation ratio to solve the problem, and further improve the efficiency by developing an upper bounded pruning approach. Then, we propose the OIR*-Tree index, which is a hybrid index combining ordered influential node lists with an R*-tree. We show that our index based approach is significantly more efficient than the greedy algorithm and the upper bounded pruning algorithm, especially when$k$is large. Finally, we evaluate the performance for all of the proposed approaches using three real datasets.

Simulation and analysis have shown that selective search can reduce the cost of large-scale distributed information retrieval. By partitioning the collection into small topical shards, and then using a resource ranking algorithm to choose a subset of shards to search for each query, fewer postings are evaluated. Here we extend the study of selective search using a fine-grained simulation investigating: selective search efficiency in a parallel query processing environment; the difference in efficiency when term-based and sample-based resource selection algorithms are used; and the effect of two policies for assigning index shards to machines. Results obtained for two large datasets and four large query logs confirm that selective search is significantly more efficient than conventional distributed search. In particular, we show that selective search is capable of both higher throughput and lower latency in a parallel environment than is exhaustive search.

The problem of maximizing bichromatic reverse k nearest neighbor queries (BR k NN) has been extensively studied in spatial databases. In this work, we present a related query for spatial-textual databases that finds an optimal location, and a set of keywords that maximizes the size of bichromatic reverse spatial textual k nearest neighbors (MaxBRST k NN). Such a query has many practical applications including social media advertisements where a limited number of relevant advertisements are displayed to each user. The problem is to find the location and the text contents to include in an advertisement so that it will be displayed to the maximum number of users. The increasing availability of spatial-textual collections allows us to answer these queries for both spatial proximity and textual similarity. This paper is the first to consider the MaxBRST k NN query. We show that the problem is NP-hard and present both approximate and exact solutions.

Social networks are a vital mechanism to disseminate information to friends and colleagues. In this work, we investigate an important problem—thepersonalized influential topic search, or PIT-Search in a social network: Given a keyword query$q$issued by a user$u$in a social network, a PIT-Search is to find the top-$k$$q$-related topics that are most influential for the query user$u$. The influence of a topic to a query user depends on the social connection between the query user and the social users containing the topic in the social network. To measure the topics’ influence at the similar granularity scale, we need to extract the social summarization of the social network regarding topics. To make effective topic-aware social summarization, we propose two random-walk based approaches: random clustering and an L-length random walk. Based on the proposed approaches, we can find a small set of representative users with assigned influential scores to simulate the influence of the large number of topic users in the social network with regards to the topic. The selected representative users are denoted as the social summarization of topic-aware influence spread over the social network. And then, we verify the usefulness of the social summarization by applying it to the problem of personalized influential topic search. Finally, we evaluate the performance of our algorithms using real-world datasets, and show the approach is efficient and effective in practice.

Sophisticated ranking mechanisms make use of term dependency features in order to compute similarity scores for documents. These features often include exact phrase occurrences, and term proximity estimates. Both cases build on the intuition that if multiple query terms appear near each other, the document is more likely to be relevant to the query. In this paper we examine the processes used to compute these statistics. Two distinct input structures can be used -- inverted files and direct files. Inverted files must store the position offsets of the terms, while "direct" files represent each document as a sequence of preprocessed term identifiers. Based on these two input modalities, a number of algorithms can be used to compute proximity statistics. Until now, these algorithms have been described in terms of a single set of query terms. But similarity computations such as the Full Dependency Model compute proximity statistics for a collection of related term sets. We present a new approach in which such collections are processed holistically in time that is much less than would be the case if each subquery were to be evaluated independently. The benefits of the new method are demonstrated by a comprehensive experimental study.

In this work, we investigate approaches to engineer better topic sets in information retrieval test collections. By recasting the TREC evaluation exercise from one of building more effective systems to an exercise in building better topics, we present two possible approaches to quantify topic "goodness": topic ease and topic set predictivity. A novel interpretation of a well known result and a twofold analysis of data from several TREC editions lead to a result that has been neglected so far: both topic ease and topic set predictivity have changed significantly across the years, sometimes in a perhaps undesirable way.

Information retrieval test collections traditionally use a combination of automatic and manual runs to create a pool of documents to be judged. The quality of the final judgments produced for a collection is a product of the variety across each of the runs submitted and the pool depth. In this work, we explore fully automated approaches to generating a pool. By combining a simple voting approach with machine learning from documents retrieved by automatic runs, we are able to identify a large portion of relevant documents that would normally only be found through manual runs. Our initial results are promising and can be extended in future studies to help test collection curators ensure proper judgment coverage is maintained across complete document collections.

The use of sampling, randomized algorithms, or training based on the unpredictable inputs of users in Information Retrieval often leads to non-deterministic outputs. Evaluating the effectiveness of systems incorporating these methods can be challenging since each run may produce different effectiveness scores. Current IR evaluation techniques do not address this problem. Using the context of distributed information retrieval as a case study for our investigation, we propose a solution based on multivariate linear modeling. We show that the approach provides a consistent and reliable method to compare the effectiveness of non-deterministic IR algorithms, and explain how statistics can safely be used to show that two IR algorithms have equivalent effectiveness.

Score-safe index processing has received a great deal of attention over the last two decades. By pre-calculating maximum term impacts during indexing, the number of scoring operations can be minimized, and the top-k documents for a query can be located efficiently. However, these methods often ignore the importance of the effectiveness gains possible when using sequential dependency models. We present a hybrid approach which leverages score-safe processing and suffix-based self-indexing structures in order to provide efficient and effective top-k document retrieval.

The suffix array is an efficient data structure for in-memory pattern search. Suffix arrays can also be used for external-memory pattern search, via two-level structures that use an internal index to identify the correct block of suffix pointers. In this paper, we describe a new two-level suffix array-based index structure that requires significantly less disk space than previous approaches. Key to the saving is the use of disk blocks that are based on prefixes rather than the more usual uniform-sampling approach, allowing reductions between blocks and subparts of other blocks. We also describe a new in-memory structure-the condensed BWT- and show that it allows common patterns to be resolved without access to the text. Experiments using 64 GB of English web text on a computer with 4 GB of main memory demonstrate the speed and versatility of the new approach. For this data, the index is around one-third the size of previous two-level mechanisms; and the memory footprint of as little as 1% of the text size means that queries can be processed more quickly than is possible with a compact FM-INDEX.

Formulating and processing phrases and other term dependencies to improve query effectiveness is an important problem in information retrieval. However, accessing word-sequence statistics using inverted indexes requires unreasonable processing time or substantial space overhead. Establishing a balance between these competing space and time trade-offs can dramatically improve system performance. In this article, we present and analyze a new index structure designed to improve query efficiency in dependency retrieval models. By adapting a class of ( ε, δ )-approximation algorithms originally proposed for sketch summarization in networking applications, we show how to accurately estimate statistics important in term-dependency models with low, probabilistically bounded error rates. The space requirements for the vocabulary of the index is only logarithmically linked to the size of the vocabulary. Empirically, we show that the sketch index can reduce the space requirements of the vocabulary component of an index of n -grams consisting of between 1 and 4 words extracted from the GOV2 collection to less than 0.01% of the space requirements of the vocabulary of a full index. We also show that larger n -gram queries can be processed considerably more efficiently than in current alternatives, such as positional and next-word indexes.

Formulating and processing phrases and other term dependencies to improve query effectiveness is an important problem in information retrieval. However, accessing these types of statistics using standard inverted indexes requires unreasonable processing time or incurs a substantial space overhead. Establishing a balance between these competing space and time trade-offs can dramatically improve system performance.

For over forty years the dominant data structure for ranked document retrieval has been the inverted index. Inverted indexes are effective for a variety of document retrieval tasks, and particularly efficient for large data collection scenarios that require disk access and storage. However, many efficiency-bound search tasks can now easily be supported entirely in memory as a result of recent hardware advances. In this paper we present a hybrid algorithmic framework for in-memory bag of-words ranked document retrieval using a self-index derived from the FM-Index, wavelet tree, and the compressed suffix tree data structures, and evaluate the various algorithmic trade-offs for performing efficient queries entirely in-memory. We compare our approach with two classic approaches to bag-of-words queries using inverted indexes, term-at-a-time (TAAT) and document-at-a-time (DAAT) query processing. We show that our framework is competitive with state-of-the-art indexing structures, and describe new capabilities provided by our algorithms that can be leveraged by future systems to improve effectiveness and efficiency for a variety of fundamental search operations.

Conjunctive Boolean queries are a key component of modern information retrieval systems, especially when Web-scale repositories are being searched. A conjunctive query q is equivalent to a | q |-way intersection over ordered sets of integers, where each set represents the documents containing one of the terms, and each integer in each set is an ordinal document identifier. As is the case with many computing applications, there is tension between the way in which the data is represented, and the ways in which it is to be manipulated. In particular, the sets representing index data for typical document collections are highly compressible, but are processed using random access techniques, meaning that methods for carrying out set intersections must be alert to issues to do with access patterns and data representation. Our purpose in this article is to explore these trade-offs, by investigating intersection techniques that make use of both uncompressed “integer” representations, as well as compressed arrangements. We also propose a simple hybrid method that provides both compact storage, and also faster intersection computations for conjunctive querying than is possible even with uncompressed representations.

In batch evaluation of retrieval systems, performance is calculated based on predetermined relevance judgements applied to a list of documents returned by the system for a query. This evaluation paradigm, however, ignores the current standard operation of search systems which require the user to view summaries of documents prior to reading the documents themselves.