We present an overview of SAP HANA's Native Store Extension (NSE). This extension substantially increases database capacity, allowing to scale far beyond available system memory. NSE is based on a hybrid in-memory and paged column store architecture composed from data access primitives. These primitives enable the processing of hybrid columns using the same algorithms optimized for traditional HANA's in-memory columns. Using only three key primitives, we fabricated byte-compatible counterparts for complex memory resident data structures (e.g. dictionary and hash-index), compressed schemes (e.g. sparse and run-length encoding), and exotic data types (e.g. geo-spatial). We developed a new buffer cache which optimizes the management of paged resources by smart strategies sensitive to page type and access patterns. The buffer cache integrates with HANA's new execution engine that issues pipelined prefetch requests to improve disk access patterns. A novel load unit configuration, along with a unified persistence format, allows the hybrid column store to dynamically switch between in-memory and paged data access to balance performance and storage economy according to application demands while reducing Total Cost of Ownership (TCO). A new partitioning scheme supports load unit specification at table, partition, and column level. Finally, a new advisor recommends optimal load unit configurations. Our experiments illustrate the performance and memory footprint improvements on typical customer scenarios.

We propose a novel latent variable model for learning latent bases for time-varying non-negative data. Our model uses a mixture multinomial as the likelihood function and proposes a Dirichlet distribution with dynamic parameters as a prior, which we call the dynamic Dirichlet prior. An expectation maximization (EM) algorithm is developed for estimating the parameters of the proposed model. Furthermore, we connect our proposed dynamic Dirichlet latent variable model (dynamic DLVM) to the two popular latent basis learning methods - probabilistic latent component analysis (PLCA) and non-negative matrix factorization (NMF). We show that (i) PLCA is a special case of the dynamic DLVM, and (ii) dynamic DLVM can be interpreted as a dynamic version of NMF. The effectiveness of the proposed model is demonstrated through extensive experiments on speaker source separation, and speech-noise separation. In both cases, our method performs better than relevant and competitive baselines. For speaker separation, dynamic DLVM shows 1.38 dB improvement in terms of source to interference ratio, and 1 dB improvement in source to artifact ratio.

Dynamic environment of the power system can be better controlled by the smart usage of electric vehicles (EVs). EVs with their diverse capabilities can create opportunities for Distribution System Operator (DSO) to improve the efficiency, economics, and sustainability of power delivery system. For instance, reactive power support can be provided by EVs quickly, when needed by the DSO. This type of ancillary service is becoming a necessity, due to higher power demands and continuous change in supply-demand environment. On the other hand, high battery price discourages consumers to purchase EVs. Therefor, creating ways to reduce the overall battery cost by generating revenue through participation in the power market, could be a win-win situation for both, EV owner and the DSO. Revenue generation for EVs could be achieved by supplying active and/or reactive power. In this paper, we present a model of EV operating as a reactive power service provider, considering the battery degradation, as a function of active power flow. The developed model includes charger technical constraints. Simulation results show optimal operating cost estimation, taking into consideration the effect of the battery degradation on charging, discharging and reactive power service of the EV under different scenarios. Results indicate significant change in active power charging and discharging patterns when the battery degradation cost is included. Revenue generating potential of providing reactive power service by EVs could be an encouraging fact for EVs to participate in reactive power market.

In this paper, we propose a conditional random field (CRF) based to identify segments within call center conversations that convey caller intent. A distinguishing aspect of our approach is the use of context information of the intent bearing segments to predict the presence or absence of intents within various segments. The context is represented through a set of phrase features that are frequently present in and around the intent bearing segments. These phrases, identified in a data-driven manner, are used along with conventional word features in a CRF based sequence labeling framework to assign intent/non-intent labels to each utterance in a conversation. Another distinguishing aspect of our approach is that instead of using 1-best label alignment, we extract N-best label alignments at the output of CRF and combine evidences from them to rank the utterances according to their intent bearing potential, so that top ranked utterances can be chosen as the intent summary. To demonstrate the effectiveness of our approach and to evaluate the influence of automatic speech recognition (ASR) errors we evaluated our approach using manually transcribed and ASR transcribed conversations. Experimental results show improved summarization accuracy using our approach. Specifically, in 92% of the manually transcribed conversations accurate summaries of just one utterance length can be extracted using the proposed approach.

We present a Vector Quantization based bimodal (speech and face) biometric recognition method which delivers high performance amidst noise, illumination variations and occlusions (disguised mode) while requiring very little training data, memory storage and complexity of operation. A transform VQ method delivers good face-recognition performance and a Text Dependent VQ method provides good recognition performance using speech. Simple fusion of two leads to a wider separation between the user-clusters in the combined feature space, leading to high performance.