arXiv subjects

Alistair Moffat

Publications and source records attributed to Alistair Moffat.

At least 19 recordsLinked to original sources

arXiv2026

Speed Thrills: Visceral Demonstrations That Get Students Excited About Efficient Algorithms

We address the problem of motivating students in Data Structures and Algorithms courses by presenting two simple problems that each have a series of improvements to a basic algorithm, leading to spectacular decreases in runtimes. Coining a new term, we refer to such sequences as being "thrills of algorithms". Seeing runtimes drop from an estimate of days (or even years) to just a few seconds has a visceral impact which conveys the importance of efficient algorithms in a way unlikely to be forgotten. The demonstrations are particularly compelling because they can be performed live in class on the lecturer's laptop. To assist staff teaching such courses we provide detailed pseudocode descriptions and complexity analyses for the various methods, and can supply implementations on request.

cs.DS↗

arXiv2025

The Effects of Demographic Instructions on LLM Personas

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.

cs.IR↗

arXiv2024

Stochastic Directly-Follows Process Discovery Using Grammatical Inference

Starting with a collection of traces generated by process executions, process discovery is the task of constructing a simple model that describes the process, where simplicity is often measured in terms of model size. The challenge of process discovery is that the process of interest is unknown, and that while the input traces constitute positive examples of process executions, no negative examples are available. Many commercial tools discover Directly-Follows Graphs, in which nodes represent the observable actions of the process, and directed arcs indicate execution order possibilities over the actions. We propose a new approach for discovering sound Directly-Follows Graphs that is grounded in grammatical inference over the input traces. To promote the discovery of small graphs that also describe the process accurately we design and evaluate a genetic algorithm that supports the convergence of the inference parameters to the areas that lead to the discovery of interesting models. Experiments over real-world datasets confirm that our new approach can construct smaller models that represent the input traces and their frequencies more accurately than the state-of-the-art technique. Reasoning over the frequencies of encoded traces also becomes possible, due to the stochastic semantics of the action graphs we propose, which, for the first time, are interpreted as models that describe the stochastic languages of action traces.

cs.AI↗

arXiv2024

How Much Freedom Does An Effectiveness Metric Really Have?

It is tempting to assume that because effectiveness metrics have free choice to assign scores to search engine result pages (SERPs) there must thus be a similar degree of freedom as to the relative order that SERP pairs can be put into. In fact that second freedom is, to a considerable degree, illusory. That's because if one SERP in a pair has been given a certain score by a metric, fundamental ordering constraints in many cases then dictate that the score for the second SERP must be either not less than, or not greater than, the score assigned to the first SERP. We refer to these fixed relationships as innate pairwise SERP orderings. Our first goal in this work is to describe and defend those pairwise SERP relationship constraints, and tabulate their relative occurrence via both exhaustive and empirical experimentation. We then consider how to employ such innate pairwise relationships in IR experiments, leading to a proposal for a new measurement paradigm. Specifically, we argue that tables of results in which many different metrics are listed for champion versus challenger system comparisons should be avoided; and that instead a single metric be argued for in principled terms, with any relationships identified by that metric then reinforced via an assessment of the innate relationship as to whether other metrics - indeed, all other metrics - are likely to yield the same system-vs-system outcome.

cs.IR↗

arXiv2023

Categorical, Ratio, and Professorial Data: The Case for Reciprocal Rank

Search engine results pages are usually abstracted as binary relevance vectors and hence are categorical data, meaning that only a limited set of operations is permitted, most notably tabulation of occurrence frequencies, with determination of medians and averages not possible. To compare retrieval systems it is thus usual to make use of a categorical-to-numeric effectiveness mapping. A previous paper has argued that any desired categorical-to-numeric mapping may be used, provided only that there is an argued connection between each category of SERP and the score that is assigned to that category by the mapping. Further, once that plausible connection has been established, then the mapped values can be treated as real-valued observations on a ratio scale, allowing the computation of averages. This article is written in support of that point of view, and to respond to ongoing claims that SERP scores may only be averaged if very restrictive conditions are imposed on the effectiveness mapping.

cs.IR↗

arXiv2023

Efficient Immediate-Access Dynamic Indexing

In a dynamic retrieval system, documents must be ingested as they arrive, and be immediately findable by queries. Our purpose in this paper is to describe an index structure and processing regime that accommodates that requirement for immediate access, seeking to make the ingestion process as streamlined as possible, while at the same time seeking to make the growing index as small as possible, and seeking to make term-based querying via the index as efficient as possible. We describe a new compression operation and a novel approach to extensible lists which together facilitate that triple goal. In particular, the structure we describe provides incremental document-level indexing using as little as two bytes per posting and only a small amount more for word-level indexing; provides fast document insertion; supports immediate and continuous queryability; provides support for fast conjunctive queries and similarity score-based ranked queries; and facilitates fast conversion of the dynamic index to a "normal" static compressed inverted index structure. Measurement of our new mechanism confirms that in-memory dynamic document-level indexes for collections into the gigabyte range can be constructed at a rate of two gigabytes/minute using a typical server architecture, that multi-term conjunctive Boolean queries can be resolved in just a few milliseconds each on average even while new documents are being concurrently ingested, and that the net memory space required for all of the required data structures amounts to an average of as little as two bytes per stored posting, less than half the space required by the best previous mechanism.

cs.IR↗

arXiv2022

Batch Evaluation Metrics in Information Retrieval: Measures, Scales, and Meaning

A sequence of recent papers has considered the role of measurement scales in information retrieval (IR) experimentation, and presented the argument that (only) uniform-step interval scales should be used, and hence that well-known metrics such as reciprocal rank, expected reciprocal rank, normalized discounted cumulative gain, and average precision, should be either discarded as measurement tools, or adapted so that their metric values lie at uniformly-spaced points on the number line. These papers paint a rather bleak picture of past decades of IR evaluation, at odds with the community's overall emphasis on practical experimentation and measurable improvement. Our purpose in this work is to challenge that position. In particular, we argue that mappings from categorical and ordinal data to sets of points on the number line are valid provided there is an external reason for each target point to have been selected. We first consider the general role of measurement scales, and of categorical, ordinal, interval, ratio, and absolute data collections. In connection with the first two of those categories we also provide examples of the knowledge that is captured and represented by numeric mappings to the real number line. Focusing then on information retrieval, we argue that document rankings are categorical data, and that the role of an effectiveness metric is to provide a single value that represents the usefulness to a user or population of users of any given ranking, with usefulness able to be represented as a continuous variable on a ratio scale. That is, we argue that current IR metrics are well-founded, and, moreover, that those metrics are more meaningful in their current form than in the proposed "intervalized" versions.

cs.IR↗

arXiv2022

Bootstrapping Generalization of Process Models Discovered From Event Data

Process mining extracts value from the traces recorded in the event logs of IT-systems, with process discovery the task of inferring a process model for a log emitted by some unknown system. Generalization is one of the quality criteria applied to process models to quantify how well the model describes future executions of the system. Generalization is also perhaps the least understood of those criteria, with that lack primarily a consequence of it measuring properties over the entire future behavior of the system when the only available sample of behavior is that provided by the log. In this paper, we apply a bootstrap approach from computational statistics, allowing us to define an estimator of the model's generalization based on the log it was discovered from. We show that standard process mining assumptions lead to a consistent estimator that makes fewer errors as the quality of the log increases. Experiments confirm the ability of the approach to support industry-scale data-driven systems engineering.

cs.AI↗

arXiv2022

A Sensitivity Analysis of the MSMARCO Passage Collection

The recent MSMARCO passage retrieval collection has allowed researchers to develop highly tuned retrieval systems. One aspect of this data set that makes it distinctive compared to traditional corpora is that most of the topics only have a single answer passage marked relevant. Here we carry out a "what if" sensitivity study, asking whether a set of systems would still have the same relative performance if more passages per topic were deemed to be "relevant", exploring several mechanisms for identifying sets of passages to be so categorized. Our results show that, in general, while run scores can vary markedly if additional plausible passages are presumed to be relevant, the derived system ordering is relatively insensitive to additional relevance, providing support for the methodology that was used at the time the MSMARCO passage collection was created.

cs.IR↗

arXiv2021

Anytime Ranking on Document-Ordered Indexes

Inverted indexes continue to be a mainstay of text search engines, allowing efficient querying of large document collections. While there are a number of possible organizations, document-ordered indexes are the most common, since they are amenable to various query types, support index updates, and allow for efficient dynamic pruning operations. One disadvantage with document-ordered indexes is that high-scoring documents can be distributed across the document identifier space, meaning that index traversal algorithms that terminate early might put search effectiveness at risk. The alternative is impact-ordered indexes, which primarily support top-k disjunctions, but also allow for anytime query processing, where the search can be terminated at any time, with search quality improving as processing latency increases. Anytime query processing can be used to effectively reduce high-percentile tail latency which is essential for operational scenarios in which a service level agreement (SLA) imposes response time requirements. In this work, we show how document-ordered indexes can be organized such that they can be queried in an anytime fashion, enabling strict latency control with effective early termination. Our experiments show that processing document-ordered topical segments selected by a simple score estimator outperforms existing anytime algorithms, and allows query runtimes to be accurately limited in order to comply with SLA requirements.

cs.IR↗

arXiv2020

Boosting Search Performance Using Query Variations

Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set. However, to date fusion has not been regarded as being cost-effective in cases where strict per-query efficiency guarantees are required, such as in web search. In this work we propose a novel solution to rank fusion by splitting the computation into two parts -- one phase that is carried out offline to generate pre-computed centroid answers for queries with broadly similar information needs, and then a second online phase that uses the corresponding topic centroid to compute a result page for each query. We explore efficiency improvements to classic fusion algorithms whose costs can be amortized as a pre-processing step, and can then be combined with re-ranking approaches to dramatically improve effectiveness in multi-stage retrieval systems with little efficiency overhead at query time. 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 with 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.

cs.IR↗

arXiv2020

RMITB at TREC COVID 2020

Search engine users rarely express an information need using the same query, and small differences in queries can lead to very different result sets. These user query variations have been exploited in past TREC CORE tracks to contribute diverse, highly-effective runs in offline evaluation campaigns with the goal of producing reusable test collections. In this paper, we document the query fusion runs submitted to the first and second round of TREC COVID, using ten queries per topic created by the first author. In our analysis, we focus primarily on the effects of having our second priority run omitted from the judgment pool. This run is of particular interest, as it surfaced a number of relevant documents that were not judged until later rounds of the task. If the additional judgments were included in the first round, the performance of this run increased by 35 rank positions when using RBP p=0.5, highlighting the importance of judgment depth and coverage in assessment tasks.

cs.IR↗

arXiv2020

Entropia: A Family of Entropy-Based Conformance Checking Measures for Process Mining

This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs. A process model has "good" precision with respect to the log it was discovered from if it does not encode many traces that are not part of the log, and has "good" recall if it encodes most of the traces from the log. By definition, the measures possess useful properties and can often be computed quickly.

cs.AI↗

arXiv2020

An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining

Given an event log as a collection of recorded real-world process traces, process mining aims to automatically construct a process model that is both simple and provides a useful explanation of the traces. Conformance checking techniques are then employed to characterize and quantify commonalities and discrepancies between the log's traces and the candidate models. Recent approaches to conformance checking acknowledge that the elements being compared are inherently stochastic - for example, some traces occur frequently and others infrequently - and seek to incorporate this knowledge in their analyses. Here we present an entropic relevance measure for stochastic conformance checking, computed as the average number of bits required to compress each of the log's traces, based on the structure and information about relative likelihoods provided by the model. The measure penalizes traces from the event log not captured by the model and traces described by the model but absent in the event log, thus addressing both precision and recall quality criteria at the same time. We further show that entropic relevance is computable in time linear in the size of the log, and provide evaluation outcomes that demonstrate the feasibility of using the new approach in industrial settings.

cs.AI↗

arXiv2016

CSA++: Fast Pattern Search for Large Alphabets

Indexed pattern search in text has been studied for many decades. For small alphabets, the FM-Index provides unmatched performance, in terms of both space required and search speed. For large alphabets -- for example, when the tokens are words -- the situation is more complex, and FM-Index representations are compact, but potentially slow. In this paper we apply recent innovations from the field of inverted indexing and document retrieval to compressed pattern search, including for alphabets into the millions. Commencing with the practical compressed suffix array structure developed by Sadakane, we show that the Elias-Fano code-based approach to document indexing can be adapted to provide new tradeoff options in indexed pattern search, and offers significantly faster pattern processing compared to previous implementations, as well as reduced space requirements. We report a detailed experimental evaluation that demonstrates the relative advantages of the new approach, using the standard Pizza&Chili methodology and files, as well as applied use-cases derived from large-scale data compression, and from natural language processing. For large alphabets, the new structure gives rise to space requirements that are close to those of the most highly-compressed FM-Index variants, in conjunction with unparalleled search throughput rates.

cs.DS↗

arXiv2016

Access Time Tradeoffs in Archive Compression

Web archives, query and proxy logs, and so on, can all be very large and highly repetitive; and are accessed only sporadically and partially, rather than continually and holistically. This type of data is ideal for compression-based archiving, provided that random-access to small fragments of the original data can be achieved without needing to decompress everything. The recent RLZ (relative Lempel Ziv) compression approach uses a semi-static model extracted from the text to be compressed, together with a greedy factorization of the whole text encoded using static integer codes. Here we demonstrate more precisely than before the scenarios in which RLZ excels. We contrast RLZ with alternatives based on block-based adaptive methods, including approaches that "prime" the encoding for each block, and measure a range of implementation options using both hard-disk (HDD) and solid-state disk (SSD) drives. For HDD, the dominant factor affecting access speed is the compression rate achieved, even when this involves larger dictionaries and larger blocks. When the data is on SSD the same effects are present, but not as markedly, and more complex trade-offs apply.

cs.IT↗

arXiv2015

Assessing Efficiency-Effectiveness Tradeoffs in Multi-Stage Retrieval Systems Without Using Relevance Judgments

Large-scale retrieval systems are often implemented as a cascading sequence of phases -- a first filtering step, in which a large set of candidate documents are extracted using a simple technique such as Boolean matching and/or static document scores; and then one or more ranking steps, in which the pool of documents retrieved by the filter is scored more precisely using dozens or perhaps hundreds of different features. The documents returned to the user are then taken from the head of the final ranked list. Here we examine methods for measuring the quality of filtering and preliminary ranking stages, and show how to use these measurements to tune the overall performance of the system. Standard top-weighted metrics used for overall system evaluation are not appropriate for assessing filtering stages, since the output is a set of documents, rather than an ordered sequence of documents. Instead, we use an approach in which a quality score is computed based on the discrepancy between filtered and full evaluation. Unlike previous approaches, our methods do not require relevance judgments, and thus can be used with virtually any query set. We show that this quality score directly correlates with actual differences in measured effectiveness when relevance judgments are available. Since the quality score does not require relevance judgments, it can be used to identify queries that perform particularly poorly for a given filter. Using these methods, we explore a wide range of filtering options using thousands of queries, categorize the relative merits of the different approaches, and identify useful parameter combinations.

cs.IR↗

arXiv2013

From Theory to Practice: Plug and Play with Succinct Data Structures

Engineering efficient implementations of compact and succinct structures is a time-consuming and challenging task, since there is no standard library of easy-to- use, highly optimized, and composable components. One consequence is that measuring the practical impact of new theoretical proposals is a difficult task, since older base- line implementations may not rely on the same basic components, and reimplementing from scratch can be very time-consuming. In this paper we present a framework for experimentation with succinct data structures, providing a large set of configurable components, together with tests, benchmarks, and tools to analyze resource requirements. We demonstrate the functionality of the framework by recomposing succinct solutions for document retrieval.

cs.DS↗