Author name cluster

Naren Ramakrishnan

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27 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

DMWM: Dual-Mind World Model with Long-Term Imagination

Lingyi Wang
Rashed Shelim
Walid Saad
Naren Ramakrishnan

Imagination in world models is crucial for enabling agents to learn long-horizon policy in a sample-efficient manner. Existing recurrent state-space model (RSSM)-based world models depend on single-step statistical inference to capture the environment dynamics, and, hence, they are unable to perform long-term imagination tasks due to the accumulation of prediction errors. Inspired by the dual-process theory of human cognition, we propose a novel dual-mind world model (DMWM) framework that integrates logical reasoning to enable imagination with logical consistency. DMWM is composed of two components: an RSSM-based System 1 (RSSM-S1) component that handles state transitions in an intuitive manner and a logic-integrated neural network-based System 2 (LINN-S2) component that guides the imagination process through hierarchical deep logical reasoning. The inter-system feedback mechanism is designed to ensure that the imagination process follows the logical rules of the real environment. The proposed framework is evaluated on benchmark tasks that require long-term planning from the DMControl suite and robotic environment. Extensive experimental results demonstrate that the proposed framework yields significant improvements in terms of logical coherence, trial efficiency, data efficiency and long-term imagination over the state-of-the-art world models.

TMLR Journal 2025 Journal Article

Is isotropy a good proxy for generalization in time series forecasting with transformers?

Rashed Shelim
Shengzhe Xu
Walid Saad
Naren Ramakrishnan

Vector representations of contextual embeddings learned by transformer-based models have been shown to be effective even for downstream tasks in \emph{numerical domains} such as time series forecasting. Their success in capturing long-range dependencies and contextual semantics has led to broad adoption across architectures. But at the same time, there is little theoretical understanding of when transformers, both autoregressive and non-autoregressive, generalize well to forecasting tasks. This paper addresses this gap through an analysis of isotropy in contextual embedding space. Specifically, we study a log-linear model as a simplified abstraction for studying hidden representations in transformer-based models. In this formulation, time series embeddings are mapped to predictive outputs through a softmax layer, providing a tractable lens for analyzing generalization. We show that state-of-the-art performance requires embeddings to possess a structure that accounts for the shift-invariance of the softmax function. By examining the gradient structure of self-attention, we demonstrate how isotropy preserves representation structure, resolves the shift-invariance problem, and provides insights into model reliability and generalization. Experiments across $22$ different numerical datasets and $5$ different transformer-based models show that data characteristics and architectural choices significantly affect isotropy, which in turn directly influences forecasting performance. This establishes isotropy as a theoretically grounded and empirically validated indicator of generalization and reliability in time series forecasting. The code for the isotropy analysis and all data are publicly available.

ICML Conference 2025 Conference Paper

LLMs Can Reason Faster Only If We Let Them

Bilgehan Sel
Lifu Huang
Naren Ramakrishnan
Ruoxi Jia 0001
Ming Jin 0002

Large language models (LLMs) are making inroads into classical AI problems such as automated planning, yet key shortcomings continue to hamper their integration. Chain-of-Thought (CoT) struggles in complex multi-step reasoning, and Tree-of-Thoughts requires multiple queries that increase computational overhead. Recently, Algorithm-of-Thoughts (AoT) have shown promise using in-context examples, at the cost of significantly longer solutions compared to CoT. Aimed at bridging the solution length gap between CoT and AoT, this paper introduces AoT-O3, which combines supervised finetuning on AoT-style plans with a reinforcement learning (RL) framework designed to reduce solution length. The RL component uses a reward model that favors concise, valid solutions while maintaining planning accuracy. Empirical evaluations indicate that AoT-O3 shortens solution length by up to 80% compared to baseline AoT while maintaining or surpassing prior performance. These findings suggest a promising pathway for more efficient, scalable LLM-based planning.

TIST Journal 2024 Journal Article

Neural Methods for Data-to-text Generation

Mandar Sharma
Ajay Kumar Gogineni
Naren Ramakrishnan

The neural boom that has sparked natural language processing (NLP) research throughout the last decade has similarly led to significant innovations in data-to-text (D2T) generation. This survey offers a consolidated view into the neural D2T paradigm with a structured examination of the approaches, benchmark datasets, and evaluation protocols. This survey draws boundaries separating D2T from the rest of the natural language generation (NLG) landscape, encompassing an up-to-date synthesis of the literature, and highlighting the stages of technological adoption from within and outside the greater NLG umbrella. With this holistic view, we highlight promising avenues for D2T research that focus not only on the design of linguistically capable systems but also on systems that exhibit fairness and accountability.

AAAI Conference 2023 Conference Paper

EINNs: Epidemiologically-Informed Neural Networks

Alexander Rodríguez
Jiaming Cui
Naren Ramakrishnan
Bijaya Adhikari
B. Aditya Prakash

We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility afforded by AI models, and their capabilities to ingest heterogeneous information. Although neural forecasting models have been successful in multiple tasks, predictions well-correlated with epidemic trends and long-term predictions remain open challenges. Epidemiological ODE models contain mechanisms that can guide us in these two tasks; however, they have limited capability of ingesting data sources and modeling composite signals. Thus, we propose to leverage work in physics-informed neural networks to learn latent epidemic dynamics and transfer relevant knowledge to another neural network which ingests multiple data sources and has more appropriate inductive bias. In contrast with previous work, we do not assume the observability of complete dynamics and do not need to numerically solve the ODE equations during training. Our thorough experiments on all US states and HHS regions for COVID-19 and influenza forecasting showcase the clear benefits of our approach in both short-term and long-term forecasting as well as in learning the mechanistic dynamics over other non-trivial alternatives.

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AAAI Conference 2022 Short Paper

Blocking Influence at Collective Level with Hard Constraints (Student Abstract)

Zonghan Zhang
Subhodip Biswas
Fanglan Chen
Kaiqun Fu
Taoran Ji
Chang-Tien Lu
Naren Ramakrishnan
Zhiqian Chen

Influence blocking maximization (IBM) is crucial in many critical real-world problems such as rumors prevention and epidemic containment. The existing work suffers from: (1) concentrating on uniform costs at the individual level, (2) mostly utilizing greedy approaches to approximate optimization, (3) lacking a proper graph representation for influence estimates. To address these issues, this research introduces a neural network model dubbed Neural Influence Blocking (NIB) for improved approximation and enhanced influence blocking effectiveness. The code is available at https: //github. com/oates9895/NIB.

AAAI Conference 2021 Conference Paper

Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications

Taoran Ji
Nathan Self
Kaiqun Fu
Zhiqian Chen
Naren Ramakrishnan
Chang-Tien Lu

Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is n-step forecasting: predicting the arrival time and the technology class of the next n citations. In this paper, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-tosequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions.

AAAI Conference 2021 Conference Paper

Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

Alexander Rodríguez
Nikhil Muralidhar
Bijaya Adhikari
Anika Tabassum
Naren Ramakrishnan
B. Aditya Prakash

Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making. However, effective influenza forecasting still remains a challenge despite increasing research interest. It is even more challenging amidst the COVID pandemic, when the influenza-like illness (ILI) counts are affected by various factors such as symptomatic similarities with COVID-19 and shift in healthcare seeking patterns of the general population. Under the current pandemic, historical influenza models carry valuable expertise about the disease dynamics but face difficulties adapting. Therefore, we propose CALI-NET, a neural transfer learning architecture which allows us to ’steer’ a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our framework enables this adaptation by automatically learning when it should emphasize learning from COVID-related signals and when it should learn from the historical model. Thus, we exploit representations learned from historical ILI data as well as the limited COVID-related signals. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic. In addition, we show that success in our primary goal, adaptation, does not sacrifice overall performance as compared with state-of-the-art influenza forecasting approaches.

TIST Journal 2020 Journal Article

Cut-n-Reveal

Nikhil Muralidhar
Anika Tabassum
Liangzhe Chen
Supriya Chinthavali
Naren Ramakrishnan
B. Aditya Prakash

Recent hurricane events have caused unprecedented amounts of damage on critical infrastructure systems and have severely threatened our public safety and economic health. The most observable (and severe) impact of these hurricanes is the loss of electric power in many regions, which causes breakdowns in essential public services. Understanding power outages and how they evolve during a hurricane provides insights on how to reduce outages in the future, and how to improve the robustness of the underlying critical infrastructure systems. In this article, we propose a novel scalable segmentation with explanations framework to help experts understand such datasets. Our method, CnR (Cut-n-Reveal), first finds a segmentation of the outage sequences based on the temporal variations of the power outage failure process so as to capture major pattern changes. This temporal segmentation procedure is capable of accounting for both the spatial and temporal correlations of the underlying power outage process. We then propose a novel explanation optimization formulation to find an intuitive explanation of the segmentation such that the explanation highlights the culprit time series of the change in each segment. Through extensive experiments, we show that our method consistently outperforms competitors in multiple real datasets with ground truth. We further study real county-level power outage data from several recent hurricanes (Matthew, Harvey, Irma) and show that CnR recovers important, non-trivial, and actionable patterns for domain experts, whereas baselines typically do not give meaningful results.

IJCAI Conference 2019 Conference Paper

DyAt Nets: Dynamic Attention Networks for State Forecasting in Cyber-Physical Systems

Nikhil Muralidhar
Sathappan Muthiah
Naren Ramakrishnan

Multivariate time series forecasting is an important task in state forecasting for cyber-physical systems (CPS). State forecasting in CPS is imperative for optimal planning of system energy utility and understanding normal operational characteristics of the system thus enabling anomaly detection. Forecasting models can also be used to identify sub-optimal or worn out components and are thereby useful for overall system monitoring. Most existing work only performs single step forecasting but in CPS it is imperative to forecast the next sequence of system states (i. e curve forecasting). In this paper, we propose DyAt (Dynamic Attention) networks, a novel deep learning sequence to sequence (Seq2Seq) model with a novel hierarchical attention mechanism for long-term time series state forecasting. We evaluate our method on several CPS state forecasting and electric load forecasting tasks and find that our proposed DyAt models yield a performance improvement of at least 13. 69% for the CPS state forecasting task and a performance improvement of at least 18. 83% for the electric load forecasting task over other state-of-the-art forecasting baselines. We perform rigorous experimentation with several variants of the DyAt model and demonstrate that the DyAt models indeed learn better representations over the entire course of the long term forecast as compared to their counterparts with or without traditional attention mechanisms. All data and source code has been made available online.

IJCAI Conference 2019 Conference Paper

Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

Taoran Ji
Zhiqian Chen
Nathan Self
Kaiqun Fu
Chang-Tien Lu
Naren Ramakrishnan

Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent's next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting.

TIST Journal 2018 Journal Article

illiad

Nikhil Muralidhar
Chen Wang
Nathan Self
Marjan Momtazpour
Kiyoshi Nakayama
Ratnesh Sharma
Naren Ramakrishnan

Cyber-physical systems (CPSs) are today ubiquitous in urban environments. Such systems now serve as the backbone to numerous critical infrastructure applications, from smart grids to IoT installations. Scalable and seamless operation of such CPSs requires sophisticated tools for monitoring the time series progression of the system, dynamically tracking relationships, and issuing alerts about anomalies to operators. We present an online monitoring system ( illiad ) that models the state of the CPS as a function of its relationships between constituent components, using a combination of model-based and data-driven strategies. In addition to accurate inference for state estimation and anomaly tracking, illiad also exploits the underlying network structure of the CPS (wired or wireless) for state estimation purposes. We demonstrate the application of illiad to two diverse settings: a wireless sensor motes application and an IEEE 33-bus microgrid.

IJCAI Conference 2018 Conference Paper

Social Media based Simulation Models for Understanding Disease Dynamics

Ting Hua
Chandan K Reddy
Lei Zhang
Lijing Wang
Liang Zhao
Chang-Tien Lu
Naren Ramakrishnan

In this modern era, infectious diseases, such as H1N1, SARS, and Ebola, are spreading much faster than any time in history. Efficient approaches are therefore desired to monitor and track the diffusion of these deadly epidemics. Traditional computational epidemiology models are able to capture the disease spreading trends through contact network, however, one unable to provide timely updates via real-world data. In contrast, techniques focusing on emerging social media platforms can collect and monitor real-time disease data, but do not provide an understanding of the underlying dynamics of ailment propagation. To achieve efficient and accurate real-time disease prediction, the framework proposed in this paper combines the strength of social media mining and computational epidemiology. Specifically, individual health status is first learned from user's online posts through Bayesian inference, disease parameters are then extracted for the computational models at population-level, and the outputs of computational epidemiology model are inversely fed into social media data based models for further performance improvement. In various experiments, our proposed model outperforms current disease forecasting approaches with better accuracy and more stability.

AAAI Conference 2016 Conference Paper

Efficient Nonparametric Subgraph Detection Using Tree Shaped Priors

Nannan Wu
Feng Chen
Jianxin Li
Baojian Zhou
Naren Ramakrishnan

Non-parametric graph scan (NPGS) statistics are used to detect anomalous connected subgraphs on graphs, and have a wide variety of applications, such as disease outbreak detection, road trafﬁc congestion detection, and event detection in social media. In contrast to traditional parametric scan statistics (e. g. , the Kulldorff statistic), NPGS statistics are free of distributional assumptions and can be applied to heterogeneous graph data. In this paper, we make a number of contributions to the computational study of NPGS statistics. First, we present a novel reformulation of the problem as a sequence of Budget Price-Collecting Steiner Tree (B- PCST) sub-problems. Second, we show that this reformulated problem is NP-hard for a large class of nonparametric statistic functions. Third, we further develop efﬁcient exact and approximate algorithms for a special category of graphs in which the anomalous subgraphs can be reformulated in a ﬁxed tree topology. Finally, using extensive experiments we demonstrate the performance of our proposed algorithms in two real-world application domains (water pollution detection in water sensor networks and spatial event detection in social media networks) and contrast against state-of-theart connected subgraph detection methods.

AAAI Conference 2016 Conference Paper

Inferring Multi-Dimensional Ideal Points for US Supreme Court Justices

Mohammad Islam
K. S. M. Hossain
Siddharth Krishnan
Naren Ramakrishnan

In Supreme Court parlance and the political science literature, an ideal point positions a justice in a continuous space and can be interpreted as a quantiﬁcation of the justice’s policy preferences. We present an automated approach to infer such ideal points for justices of the US Supreme Court. This approach combines topic modeling over case opinions with the voting (and endorsing) behavior of justices. Furthermore, given a topic of interest, say the Fourth Amendment, the topic model can be optionally seeded with supervised information to steer the inference of ideal points. Application of this methodology over ﬁve years of cases provides interesting perspectives into the leaning of justices on crucial issues, coalitions underlying speciﬁc topics, and the role of swing justices in deciding the outcomes of cases.

AAAI Conference 2016 Conference Paper

Topical Analysis of Interactions Between News and Social Media

Ting Hua
Yue Ning
Feng Chen
Chang-Tien Lu
Naren Ramakrishnan

The analysis of interactions between social media and traditional news streams is becoming increasingly relevant for a variety of applications, including: understanding the underlying factors that drive the evolution of data sources, tracking the triggers behind events, and discovering emerging trends. Researchers have explored such interactions by examining volume changes or information diffusions, however, most of them ignore the semantical and topical relationships between news and social media data. Our work is the ﬁrst attempt to study how news inﬂuences social media, or inversely, based on topical knowledge. We propose a hierarchical Bayesian model that jointly models the news and social media topics and their interactions. We show that our proposed model can capture distinct topics for individual datasets as well as discover the topic inﬂuences among multiple datasets. By applying our model to large sets of news and tweets, we demonstrate its signiﬁcant improvement over baseline methods and explore its power in the discovery of interesting patterns for real world cases.

IS Journal 2015 Journal Article

Model-Based Forecasting of Significant Societal Events

Naren Ramakrishnan
Chang-Tien Lu
Madhav Marathe
Achla Marathe
Anil Vullikanti
Stephen Eubank
Scotland Leman
Michael Roan

The article outlines some salient aspects of Embers-generated forecasts through its design considerations, system architecture, and user interface.

TIST Journal 2014 Journal Article

AutoLCA

M. Shahriar Hossain
Manish Marwah
Amip Shah
Layne T. Watson
Naren Ramakrishnan

With increasing public consciousness regarding sustainability, companies are ever more eager to introduce eco-friendly products and services. Assessing environmental footprints and designing sustainable products are challenging tasks since they require analysis of each component of a product through their life cycle. To achieve sustainable design of products, companies need to evaluate the environmental impact of their system, identify the major contributors to the footprint, and select the design alternative with the lowest environmental footprint. In this article, we formulate sustainable design as a series of clustering and classification problems, and propose a framework called AutoLCA that simplifies the effort of estimating the environmental footprint of a product bill of materials by more than an order of magnitude over current methods, which are mostly labor intensive. We apply AutoLCA to real data from a large computer manufacturer. We conduct a case study on bill of materials of four different products, perform a “hotspot” assessment analysis to identify major contributors to carbon footprint, and determine design alternatives that can reduce the carbon footprint from 1% to 36%.

TIST Journal 2014 Journal Article

Charging and Storage Infrastructure Design for Electric Vehicles

Marjan Momtazpour
Patrick Butler
Naren Ramakrishnan
M. Shahriar Hossain
Mohammad C. Bozchalui
Ratnesh Sharma

Ushered by recent developments in various areas of science and technology, modern energy systems are going to be an inevitable part of our societies. Smart grids are one of these modern systems that have attracted many research activities in recent years. Before utilizing the next generation of smart grids, we should have a comprehensive understanding of the interdependent energy networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this article, we present a novel framework to support charging and storage infrastructure design for electric vehicles. We develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. Furthermore, we evaluate the network before and after the deployment of charging stations, to recommend the installation of appropriate storage units to overcome the extra load imposed on the network by the charging stations. We demonstrate the multiple factors that can be simultaneously leveraged in our framework to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.

AAAI Conference 2013 Conference Paper

A Temporal Motif Mining Approach to Unsupervised Energy Disaggregation: Applications to Residential and Commercial Buildings

Huijuan Shao
Manish Marwah
Naren Ramakrishnan

Non-intrusive appliance load monitoring has emerged as an attractive approach to study energy consumption patterns without instrumenting every device in a building. The ensuing computational problem is to disaggregate total energy usage into usage by speciﬁc devices, to gain insight into consumption patterns. We exploit the temporal ordering implicit in on/off events of devices to uncover motifs (episodes) corresponding to the operation of individual devices. Extracted motifs are then subjected to a sequence of constraint checks to ensure that the resulting episodes are interpretable. Our results reveal that motif mining is adept at distinguishing devices with multiple power levels and at disentangling the combinatorial operation of devices. With suitably conﬁgured processing steps, we demonstrate the applicability of our method to both residential and commercial buildings.

AAAI Conference 2013 Conference Paper

Clustering with Complex Constraints — Algorithms and Applications

Weifeng Zhi
Xiang Wang
Buyue Qian
Patrick Butler
Naren Ramakrishnan
Ian Davidson

Clustering with constraints is an important and developing area. However, most work is conﬁned to conjunctions of simple together and apart constraints which limit their usability. In this paper, we propose a new formulation of constrained clustering that is able to incorporate not only existing types of constraints but also more complex logical combinations beyond conjunctions. We ﬁrst show how any statement in conjunctive normal form (CNF) can be represented as a linear inequality. Since existing clustering formulations such as spectral clustering cannot easily incorporate these linear inequalities, we propose a quadratic programming (QP) clustering formulation to accommodate them. This new formulation allows us to have much more complex guidance in clustering. We demonstrate the effectiveness of our approach in two applications on text and personal information management. We also compare our algorithm against existing constrained spectral clustering algorithm to show its efﬁciency in computational time.

IS Journal 2013 Journal Article

Recent Advances in Computational Epidemiology

Madhav V. Marathe
Naren Ramakrishnan

Public health epidemiology aims to understand the spatiotemporal spread of diseases and to develop methods to control such spread. Computational epidemiology has become increasingly multidisciplinary and has led to novel computational methods for understanding and controlling spatiotemporal disease spread. Recent advances focus specifically on modeling, data mining, and inferential and planning questions.

AAAI Conference 2012 Conference Paper

Fine-Grained Photovoltaic Output Prediction Using a Bayesian Ensemble

Prithwish Chakraborty
Manish Marwah
Martin Arlitt
Naren Ramakrishnan

Local and distributed power generation is increasingly reliant on renewable power sources, e. g. , solar (photovoltaic or PV) and wind energy. The integration of such sources into the power grid is challenging, however, due to their variable and intermittent energy output. To effectively use them on a large scale, it is essential to be able to predict power generation at a finegrained level. We describe a novel Bayesian ensemble methodology involving three diverse predictors. Each predictor estimates mixing coefficients for integrating PV generation output profiles but captures fundamentally different characteristics. Two of them employ classical parameterized (naive Bayes) and non-parametric (nearest neighbor) methods to model the relationship between weather forecasts and PV output. The third predictor captures the sequentiality implicit in PV generation and uses motifs mined from historical data to estimate the most likely mixture weights using a stream prediction methodology. We demonstrate the success and superiority of our methods on real PV data from two locations that exhibit diverse weather conditions. Predictions from our model can be harnessed to optimize scheduling of delay tolerant workloads, e. g. , in a data center.

AAAI Conference 2011 Conference Paper

Discovering Life Cycle Assessment Trees from Impact Factor Databases

Naren Sundaravaradan
Debprakash Patnaik
Naren Ramakrishnan
Manish Marwah
Amip Shah

In recent years, environmental sustainability has received widespread attention due to continued depletion of natural resources and degradation of the environment. Life cycle assessment (LCA) is a methodology for quantifying multiple environmental impacts of a product, across its entire life cycle – from creation to use to discard. The key object of interest in LCA is the inventory tree, with the desired product as the root node and the materials and processes used across its life cycle as the children. The total impact of the parent in any environmental category is a linear combination of the impacts of the children in that category. LCA has generally been used in ‘forward’ mode: given an inventory tree and impact factors of its children, the task is to compute the impact factors of the root, i. e. , the product being modeled. We propose a data mining approach to solve the inverse problem, where the task is to infer inventory trees from a database of environmental factors. This is an important problem with applications in not just understanding what parts and processes constitute a product but also in designing and developing more sustainable alternatives. Our solution methodology is one of feature selection but set in the context of a non-negative least squares problem. It organizes numerous non-negative least squares ﬁts over the impact factor database into a set of pairwise membership relations which are then summarized into candidate trees in turn yielding a consensus tree. We demonstrate the applicability of our approach over real LCA datasets obtained from a large computer manufacturer.

TIST Journal 2011 Journal Article

Temporal data mining approaches for sustainable chiller management in data centers

Debprakash Patnaik
Manish Marwah
Ratnesh K. Sharma
Naren Ramakrishnan

Practically every large IT organization hosts data centers---a mix of computing elements, storage systems, networking, power, and cooling infrastructure---operated either in-house or outsourced to major vendors. A significant element of modern data centers is their cooling infrastructure, whose efficient and sustainable operation is a key ingredient to the “always-on” capability of data centers. We describe the design and implementation of CAMAS (Chiller Advisory and MAnagement System), a temporal data mining solution to mine and manage chiller installations. CAMAS embodies a set of algorithms for processing multivariate time-series data and characterizes sustainability measures of the patterns mined. We demonstrate three key ingredients of CAMAS---motif mining, association analysis, and dynamic Bayesian network inference---that help bridge the gap between low-level, raw, sensor streams, and the high-level operating regions and features needed for an operator to efficiently manage the data center. The effectiveness of CAMAS is demonstrated by its application to a real-life production data center managed by HP.

AAAI Conference 2004 Conference Paper

Spatial Aggregation for Qualitative Assessment of Scientific Computations

Chris Bailey-Kellogg
Naren Ramakrishnan

Qualitative assessment of scientific computations is an emerging application area that applies a data-driven approach to characterize, at a high level, phenomena including conditioning of matrices, sensitivity to various types of error propagation, and algorithmic convergence behavior. This paper develops a spatial aggregation approach that formalizes such analysis in terms of model selection utilizing spatial structures extracted from matrix perturbation datasets. We focus in particular on the characterization of matrix eigenstructure, both analyzing sensitivity of computations with spectral portraits and determining eigenvalue multiplicity with Jordan portraits. Our approach employs spatial reasoning to overcome noise and sparsity by detecting mutually reinforcing interpretations, and to guide subsequent data sampling. It enables quantitative evaluation of properties of a scientific computation in terms of confidence in a model, explainable in terms of the sampled data and domain knowledge about the underlying mathematical structure. Not only is our methodology more rigorous than the common approach of visual inspection, but it also is often substantially more efficient, due to well-defined stopping criteria. Results show that the mechanism efficiently samples perturbation space and successfully uncovers high-level properties of matrices.

IJCAI Conference 2003 Conference Paper

Gaussian Process Models of Spatial Aggregation Algorithms

Naren Ramakrishnan
Chris Bailey-Kellogg

Multi-level spatial aggregates are important for data mining in a variety of scientific and engineering applications, from analysis of weather data (aggregating temperature and pressure data into ridges and fronts) to performance analysis of wireless systems (aggregating simulation results into configuration space regions exhibiting particular performance characteristics). In many of these applications, data collection is expensive and time consuming, so effort must be focused on gathering samples at locations that will be most important for the analysis. This requires that we be able to functionally model a data mining algorithm in order to assess the impact of potential samples on the mining of suitable spatial aggregates. This paper describes a novel Gaussian process approach to modeling multi-layer spatial aggregation algorithms, and demonstrates the ability of the resulting models to capture the essential underlying qualitative behaviors of the algorithms. By helping cast classical spatial aggregation algorithms in a rigorous quantitative framework, the Gaussian process models support diverse uses such as directed sampling, characterizing the sensitivity of a mining algorithm to particular parameters, and understanding how variations in input data fields percolate up through a spatial aggregation hierarchy.